Crossfade: Observing Transitions from Broadcasting to an Algorithmic “Hot Clock”
Analysis of Spotify’s Content Curation Algorithms
Master Thesis
University of Amsterdam Graduate School of Humanities Media Studies: New Media and Digital Culture
Author: Oskar Štrajn Student Number: 10849912 E-mail: [email protected]
Supervisor: Anne Helmond, MA Second Reader: David Nieborg, PhD Amsterdam, 26 June 2015
ABSTRACT
In a society in which digital technology is highly embedded in the everyday life, the processes of automated information forwarding represent a significant form of authority. By observing the current models of content forwarding that employ algorithms, we can identify that these systems are imitating structures already used by the traditional broadcast media. In this thesis, with the help of Spotify’s algorithms as a case study, I present a comparison of algorithms with the traditional FM radio “hot clock”; a schema indicating when particular music selections are to be aired. Furthermore, similar to when broadcast media profoundly influenced popular culture and everyday habits in the pre-internet, software on influences contemporary culture; the latter phenomenon shall be observed in this thesis.
KEYWORDS: algorithms, context curation, algorithmic culture, Spotify, music
ACKNOWLEDGEMENTS:
I would like express my deepest gratitude to everyone who encouraged, supported and assisted me to writing this thesis. I would also like to extend special thanks to my supervisor Anne Helmond for guiding and advising me on how to express my thoughts.
Mojca and Zmago Štrajn
Blaž Blokar
Gabi Rolih
Klemen Šali
Aleks Jakulin, PhD
Melita Zajc, PhD
TABLE OF CONTENTS
1. INTRODUCTION 6
1.1. CLOUD MUSIC SERVICES 7 1.2. BRIEF HISTORY 10 1.3. DIGITAL MUSIC UBIQUITY 11 1.4. MUSIC STREAMING PLATFORMS ARE SOFTWARE 12 1.5. SOFTWARE AND ALGORITHMS 13 1.6. RESEARCH QUESTION 16 1.7. OVERVIEW 16
2. LOOKING INTO THE SOFTWARE 18
2.1. THE ERA OF ALGORITHMS 18 2.2. RECOMMENDATION ALGORITHMS OF STREAMING MUSIC SERVICES 21 2.3. SPOTIFY’S RECOMMENDATION ALGORITHMS 22 2.3.1. PERSONALIZATION ALGORITHM 23 2.3.2. CONTEXT ALGORITHM 27
3. METHODOLOGY 31
3.1. INTERFACE ANALYSIS 31 3.2. ALGORITHM ANALYSIS 33 3.3. LIMITATIONS 38
4. CONTEXT AND CURATION 39
4.1. FRONT-END INTERFACE ANALYSIS 39 4.1.1. FEATURED PLAYLISTS ANALYSIS 42 4.1.2. ANALYSIS SPONSORED CONTENT ALGORITHMS 45 4.1.3. INTRODUCING SPOTIFY RUNNING 47 4.2. BACK-END API DATA ANALYSIS AND CONTEXT-CURATED RECOMMENDER 48 4.2.1. ADVANCED CONTEXT-CURATED RECOMMENDER 53
5. THE PAST IS THE FUTURE 55
5.1. THE CURATORS 55 5.2. MEDIA SOFTWARE AND TRADITIONAL BROADCAST STREAM 57 5.3. MOVING BEYOND BROADCAST MEDIA 59
6. CONCLUSION 61
7. BIBLIOGRAPHY 64
8. APPENDIXES 69
8.1. APPENDIX 1 - RESEARCH SOFTWARE CODE 69 8.2. APPENDIX 2 - RESEARCH SOFTWARE FULL DATASET 69 8.3. APPENDIX 3 - EXCEL COLOR CODED DATA 69
TABLE OF FIGURES
FIGURE 1 – ALGORITHMS DIAGRAM 14
FIGURE 2 - SPOTIFY DISCOVER FUNCTIONALITY 24
FIGURE 3 – FEATURED PLAYLISTS SECTION 28
FIGURE 4 –PLAYLIST CREATORS 29
FIGURE 5 - CREATION OF RESEARCH ACCOUNT 32
FIGURE 6 – SCRAPING ALGORITHM SCHEME BY SANDVIG AT. AL. 33
FIGURE 7 - SPOTIFY CONTEXT TARGETING FOR BRANDS 34
FIGURE 8 - SPOTIFY DEVELOPER APPLICATION 35
FIGURE 9 - RESEARCH SERVER ROOT 35
FIGURE 10 - VIRTUAL PRIVATE SERVER (VPS) 36
FIGURE 11 - COLOR-CODED DATA IN EXCEL 37
FIGURE 12 - MAINTENANCE ANNOUNCEMENT 38
FIGURE 13 - SPOTIFY OPENING SCREEN 40
FIGURE 14 - DISCOVER WITHOUT USER’S LISTENING HISTORY 41
FIGURE 15 - BROWSING THROUGH SIMILAR ARTISTS 42
FIGURE 16 - SECTIONS OF FEATURED PLAYLISTS 43
FIGURE 17 - FEATURED PLAYLISTS EDITORS 44
FIGURE 18 - SPONSORED PLAYLISTS SUBJECTED TO CONTEXT ALGORITHM 45
FIGURE 19 - USERNAME CHANGE WHILE LOADING PLAYLIST 46
FIGURE 20 - CHART GRAPH OF PLAYLIST GROUPS WITHIN THE RESEARCH WEEK 48
FIGURE 21 - EXCEL COLOR FILTER 49
FIGURE 22 - PARTY PLAYLISTS 50
FIGURE 23 - GETTING READY AND COMMUTE PLAYLISTS 51
FIGURE 24 - OVERVIEW OF THE DATA 52
FIGURE 25 - CONTEXT DATA FROM 27TH OF APRIL 54
FIGURE 26 - ROAD TRIP PLAYLISTS 58
5 1. INTRODUCTION
In a society in which digital technology is highly embedded in everyday life, the processes of automated information forwarding representing a significant form of authority. By observing the current models of content forwarding that employ algorithms, we can identify that these systems are imitating structures already used by the traditional broadcast media. In this thesis, with the help of Spotify’s algorithms as a case study, I present a comparison of algorithms with the traditional FM radio “hot clock”; a schema indicating when particular music selections are to be aired. Furthermore, similar to when broadcast media profoundly influenced popular culture and everyday habits in the pre-internet, software on influences contemporary culture; the latter phenomenon shall be observed in this thesis.
In recent years, the usage of cloud music services has rapidly expanded, promising a new era of listening. Like physical music media, digital ones have also experienced an evolution. Here the discussion is not only about the development of new file types (.mp1, .mp2, .mp3, .flac, etc.), but about vast music databases managed by a music streaming platform. When I am referring to online streaming media systems, I mean those that offer music as their service. In particular, I am looking into already established platforms, the aim of which is to replace direct online file exchanges. The databases that offer the direct access to content are called “cloud databases”. As all digital technology is moving towards cloud computing, music listening is also following this trend (“European Cloud Computing Strategy”). The first music streaming platforms that started to offer online streaming were introduced in the early 2000s, although they only recently have become widely used, as explained in his reviews of a music cloud service (Haupt 132). In this thesis, I also refer to the music cloud services with the terms “social music platforms” or “music-streaming platforms or services”. Cloud music services, such as Pandora, founded in 2000, offering the first personalized online radio, and the music social network Last.fm, founded in 2001, introduced themselves to the public as a modern supplement to radio stations, but without the possibility of being ubiquitous like the traditional radio. Nevertheless, they offered something completely new: a personalized experience of radio streaming (Haupt 132). Last.fm did not attain great commercial success, although it was very popular from 2006 and 2008. By the end of the latter year, it had made a loss of two million pounds (UK) loss, so they chose to close its radio service and focus only on music recommendations (Sweney). Despite Last.fm no being able to create a digital environment that would attract a great
6 number of users, a new cloud music service, Spotify, founded in 2006, has experienced significant success (Haupt 138).
Once that the first streaming music platform experienced success, other services also became interesting for users. As introduced in the IFPI Digital Music Report 2014, streaming and subscription platforms are currently overtaking the music market, mainly by global brands such as Deezer and Spotify (“Digital Music Report — IFPI — Representing the Recording Industry Worldwide”). There are a number of reasons streaming services have become a matter of interest for users. Streaming music platforms offer legal on-demand music, which only became possible after the Internet. Before, one would need to own all music releases on physical media to have the same experience. However, this was already possible in pre-streaming times, where music was available through illegal channels. Music streaming platforms here helped with legalization by paying the artists royalties and aiding in the organization of an extensive content database.
This thesis is concerned with the concepts of content “curation” being made by the platforms. Curation is a process of organizing and displaying information relevant to the user. Specifically, within the music streaming platforms, I address the curation of content that is displayed by software. In order for them to function, the music streaming services are using algorithms to present the information; due to the popularity of these services, algorithms are becoming influential in the creation of popular culture.
1.1. CLOUD MUSIC SERVICES
Cloud music services appeared as a result of the evolution of digital technology and have resulted in the creation of new ways of music consumption. The first services appeared in 2002 and introduced a new digital format that allowed listening music without transferring the files to a personal device. This can be seen as a next step in the development of music formats, from vinyl to track recordings, from cassettes to CDs and minidiscs, mp3 files, to online streaming. Streaming is still based on digital music formats, as the music streamed is saved in mp3, wav, or similar files. The main difference here is that the principle of music ownership has changed, as the files are not located on the user’s computer, but inside a digital cloud. The interest in the creation of music streaming services appeared due to the disruptions in the music industry created by online piracy. Streaming music platforms managed to create an environment that was capable of managing artists’
7 rights while simultaneously satisfying users’ expectations of free music on-demand. By free here I have in mind, add-supported music streams.
This means that any user is able to play any song at any time or place, as if the user would own the entire database. Another advantage that cloud services provided was the possibility of accessing the cloud database whenever an Internet connection was available. This advantage that was offered by music streaming platforms (the possibility of streaming music at any time or place) made this software as ubiquitous as traditional radio.
Cloud music services or streaming music platforms are a combination of two different fields within the creative industries (Ahvenniemi et al. 170). In the article Creative industries and bit bang – how value is created in the digital age, the authors explain how the music industry is connected with technology and why software also is a part of the new music industry (Ahvenniemi et al. 173). Although the fields are becoming so close to each other, I am not focusing on their relationship within this thesis. I decided to pay attention to the software industry, since the platforms are its creation. However, it must be acknowledged that the content that they offer is created by the music industry, and that as music streaming platforms are becoming one of the main distributers of digital music, they also have to be analysed from music industry’s perspective (“Digital Music Report — IFPI — Representing the Recording Industry Worldwide”). In order to do so, in some cases I acknowledge the music industry perspective to exemplify the motivation for software developments. Furthermore, when I am talking about the music industry, I am referring to the music rights holder and, in the most cases, I introduce their interests and views with the usage of statements presented by the International Federation of the Phonographic Industry (IFPI). IFPI presents itself as the voice of recording industry worldwide since it represents the interests of 1,300 record companies from across the globe (“About — IFPI — Representing the Recording Industry Worldwide”). As John B. Meisel and Timothy S. Sullivan noted, the music industry, which is institutionalized and in that sense traditional, has been forced to reorganize due to the Internet and digital developments (22). The software industry is establishing new models of music consumption with streaming music platforms and consequently creating development within the music industry.
The software that is created for content forwarding should not be considered only as a product of industry but as a mediator of the content (Manovich, Software Takes Command 10). Consequently, I am using the field of software studies introduced by Lev Manovich to examine the cloud music services as new media objects, i.e. a specific media channel meant for music. I explain his views later in this chapter. I am here connecting the
8 two fields of creative industries (the software and music industries); it is the combination of communication technologies and content that is creating a new medium. This places cloud music services into a group of media software that was defined and categorized by Lev Manovich (Software Takes Command 24).
Just as media needs content, so too does content also need media; this applies to media software as much as any other form of media or content. In the case of cloud music services and the evolution of software, a point has been reached where the software has begun to overtake the traditional means of music consumption (Manovich, Media after Software 4). This is the first point about why we have to think about software as a creator of popular music culture, as we previously had thought about the traditional radio or television (Gross, Gross, and Perebinossoff 21). Later in this thesis, I make the comparison between media software and traditional broadcasts more explicit; to do so I use Lynne Gross’, Brian Grossand’s and Philippe Perebinossoff’s book Programming for TV, Radio & The Internet. However, because the software is the primary concern in this thesis, we have to acknowledge that software is running on prewritten scripts that automate the entire process of data forwarding. In other words, the processes that are empowering the software are curating the user’s daily life, creating popular culture and indirectly affecting society. In this sense, cloud music services are becoming an automated music culture curator, which is proven in the later stages of the thesis.
One of the features responsible for this curation is the recommendation system, which offers a filtered set of results. In the software, we can find a number of different recommendation systems, from personalized filters to featured content filters, which are responsible for the curation of contemporary popular culture. The part of the software responsible for the recommendation tasks as well as for the curation of the moment are algorithms and, using the terminology of Alexander Galloway, what they produce is the algorithmic culture (Gaming Essays on Algorithmic Culture 4). One of the major goals of this thesis is to closely observe music curation algorithms within a music streaming software and to recognize the patterns of curatorship that occur as a result. Although there are currently a number of services offering music streaming and there is an extensive amount of newcomers, I chose to observe one of the currently most recognizable music streaming services: Spotify.
9 1.2. BRIEF HISTORY
Music streaming services appeared as a result of digitalization. However, the digitalization was not solely the reason this software appeared; it was created as a consequence of disruptions in the music industry that happened because of digitalization.
Until 1999, the music industry had enjoyed the evolution of digital technology, as major labels started to sell music in digital formats, avoiding costs of physical media (Hracs 445). For the music industry, digital formats were seen as a tool of lowering the production costs and of increasing consumer prices, resulting in enormous profits as industry was creating its revenue from selling rights to the content it owned (Hracs 445). Although those digital formats seemed as a profitable technology in 1999, the music industry was faced with the so-called ‘MP3 Crisis’. The main issue had to do with the copyrighted material and the emergence of software formats that were easy to reproduce and share (Leyshon et al. 178). With the rapid development of personal computer technology and growing numbers of Internet users, online piracy also began to increase exponentially (Leyshon et al. 178). The music industry struggled with the crisis based on the traditional ways of dealing with copyright infringement, which did not result in positive customer relations (Makki). The music industry fought with lawsuits against software developers and users. One of the ideas of music industry was the introduction of the subscription model software that would allow users to access the music with the pay-per-song model, similar as in the early digital age (Hracs 449). One of the biggest music online resellers continues to successfully offer music based on this model: Apple’s iTunes Store.
Regardless of the music industry’s endeavours to decrease the amount of illegal content, its measures did not produce sufficient results (Hracs 449). However, independent and innovative developers introduced new models of online music consumption that attempted to endorse the interests of the music industry while simultaneously satisfying users’ expectations of free data exchange. Pressure coming from the music industry and from Internet users challenged developers to develop a concept that would resolve this crisis. On this, Spotify was built.
Spotify was designed from the ground up to combat piracy. Founded in Sweden, the home of The Pirate Bay, we believed that if we could build a service which was better than piracy, then we could convince people to stop illegal file-sharing, and start consuming music legally again. (“Spotify Explained”) When Spotify was released, it announced that with this software the music industry is saved in terms of copyright regulation. Of course this was not the case, as Spotify alone could not
10 change the whole industry; however, due to its high recognition and successful model, it is appealing case for analysis.
1.3. DIGITAL MUSIC UBIQUITY
The ability to connect with the Internet has become a common practice in recent decades; however, using music cloud services to stream music at any time is a more recent technological development. The ability to stream music, not only on a smartphone, but also in the car, throughout the house and even during a taxi ride, brings the ubiquity of digital music to another level. This can be seen as another functionality that brought cloud music services closer to the traditional radio stations that were available for listening in every environment equipped with a transistor received. In this sense, digital technology was lacking the simplicity that was offered by analogue radio transistor, however with further digital development, music streaming offered new types of listening devices to be embedded into the selected environment.
In general, cloud music services allow their users to listen to music without transferring the files and offer them to stream music from various devices. This means that the cloud music services (and their software) have become ubiquitous. In software terminology, “ubiquity” means that the users can utilize the software at almost any time and any place; in this case, providing a nomadic digital experience (Niemelä and Latvakoski 71). To further develop this idea of ubiquitous streaming, we have to look into a more general discussion about music. Anahid Kassabian first introduced the term of “ubiquitous listening”, explaining that music (in comparison to most cultural products) is capable of being omnipresent in our lives and furthermore is consumed alongside or simultaneously with other activities (Kassabian 10). Software ubiquity combines computer technology that is used in everyday life, e.g. smartphone, personal computer, work computer, car, tablet, personal player, etc.; however, in this context we are talking about the continuous listening in everyday life contexts (Niemelä and Latvakoski 71). This is why it is necessary to distinguish between the ubiquitous software, which provides access to the software from all the places where devices are capable of connecting to the online network, and the ubiquitous listening, which is connected with the content, enabling us to add a background soundtrack to every moment of our lives. For the purposes of this thesis, I will use both terms to describe functionalities within the discussed software.
11 This terminology of ubiquitous listening and ubiquitous software is of great significance to this thesis as music cloud services are becoming increasing engaged in our lives through the omnipresent concept of music listening habits and are now trying to engage with users in as many situations as possible. The connection between ubiquitous listening and software is used to address the context of listening, according to Kassabian:
In general, music apps vary in relation to the level of activity they require, duration of interest they are likely to command, the degree of attention they may occupy, and so on. …it becomes clear, that it may well be productive to think of a group of iPhone apps as a cross between wearable and pervasive computing – on the one hand they are small and always with you, like wearable computing, and they can respond to your mood – but they both interact with and create your environment. […] IPhone apps are a new ‘size’ of interaction with environment, a new place of processing between wearable and pervasive computing, a new set of audio-visual relations, and a new form of soundscape management. (Kassabian 16) Kassabian here explains how music has become even more involved in our life, since it is possible to access any kind of music with a device that has the access to it. Nevertheless, his theory is more connected with the description of music, saying that there are music and sounds that are capable to fit in every environment.
1.4. MUSIC STREAMING PLATFORMS ARE SOFTWARE
As I presented beforehand, cloud music platforms are online applications that offer music listening as a service. Furthermore, they are available on a number of different devices, which makes them ubiquitous, and they offer a wide range of content. In order to make the content interesting and convenient for the listener (also referred to as the “user” in this thesis), platforms offer a number of different techniques to organize and curate the data. As music-streaming platforms are software, content distribution within them is accomplished via the usage of pre-determined protocols, also known as algorithms. When I am referring to “algorithms”, I have in mind the explanation of Tarleton Gillespie, which presents algorithms as search engines of massive databases that manage our interactions on social networking sites and help us discover what is currently popular (Gillespie). He also discusses a particular type of algorithms, recommendation algorithms that are “suggesting new or forgotten bits of culture for us to encounter” (Gillespie). Software, as such, employs algorithms to provide user relevant content. As a result, software has already been a part of a vast number of debates on its influences on contemporary society. When I am writing about “society”, I refer to the 21st-century Western society and the nations influenced by it. In order to discuss the software, I am examining it with the usage of Lev Manovich’s
12 theoretical framework of software studies. He extended the theory of media studies by categorizing software and proclaimed it to be one of the influences of modern society (Manovich, Software Takes Command 20). In his book Software Takes Command, he argues that software currently is one of the engines of culture creation, and he termed this subset of software as a “cultural software” (Manovich, Software Takes Command 21). Furthermore, he presented another sublevel of cultural software, that he calls “media software”, which is being used for creating, editing, organizing, distributing, accessing and combining media content (Manovich, Software Takes Command 24). In this thesis, I look at the streaming music platforms as media software because they are enabling users to access music, record companies to distribute it, and, furthermore, they create capacity for classification and organization of songs.
As previously mentioned, I took the music-streaming platform, Spotify, as the object of study for this thesis. As a well-established software, that by June 10th 2015 had 20 million subscribers and more than 75 million active users where available, it has created a digital environment that attracts new listeners (The Spotify Team). This environment is empowered by algorithms that are managing the content, linking music with users through specific channels within the software. Spotify is in this thesis as well described as well as a service, due to its delivery model; a subscription based software, known also by the name ”on- demand software" The aim of this research is to focus on the recommendation algorithms that are responsible for music curation.
1.5. SOFTWARE AND ALGORITHMS
In order to discuss the topic of algorithms, I sketched an algorithm diagram (Figure 1), which I use through the text. As previously presented, a number of Spotify’s features are driven by algorithms. All algorithms employed in the software are a part of the first level of the algorithm diagram. Some of them are specifically employed to recommend music to users. These are the recommendation algorithms. They are a subgroup of all software algorithms, and thus are on the second level. There are a number of different recommendation algorithms currently available. In this thesis, however, I discuss two in particular. One type, which is already a part of many debates, is the personalization algorithm, most known among academics due to the work of Ali Pariser. He discusses personalization in depth within his book The Filter Bubble. I am contextualizing personalization with the help of Ali Pariser and Feuz Martin, Fuller Matthew, and Stalder
13 Felix, in their study of Google Search entitled Personal web searching in the age of semantic capitalism: Diagnosing the mechanisms of personalization.
FIGURE 6 – ALGORITHMS DIAGRAM
At the same level of personalization, I argue that there is another important recommendation algorithm that remains under-discussed. This the algorithm links users’ context with the software database in order to provide it with the most relevant matches. With the word “context”, I refer to the circumstances in which a person currently is. That can be a physical place or event, or a feeling or mood. To address this type of algorithm through this thesis, I use the term “context-curated algorithm” or “recommender”. The name is a combination of words context, where the circumstances are addressed, and curation as a process of organizing and displaying content. As a case study to present context-curated algorithms, I will use Spotify’s Featured Playlists, which can be seen on the fourth level. On this level of the algorithm diagram, there are some examples of algorithms currently in use. To address these algorithms. I am using Spotify’s Developer terminology found on their web page (“Spotify Developer”). Spotify’s developers have named the
14 algorithm responsible for personalized recommendations the “Discover” algorithm, and the one responsible for suggesting playlists the “Featured Playlists” algorithm.
As previously mentioned, I am focusing on Spotify’s Featured Playlists algorithm in this research, because it is linked not only with automated data forwarding but also with editorial suggestions. This is the reason we can find an external entity within the algorithm diagram (Figure 1). With “editorial suggestions”, I am referring to the music specialists who are responsible for contributions to music curation. Tarleton Gillepie recognized this difference between algorithmic and editorial logic in his article The Relevance of Algorithms (Gillespie). Based on what he calls ‘knowledge logic’:
Both struggle with, and claim to resolve, the fundamental problem of human knowledge: how to identify relevant information crucial to the public, through unavoidably human means, in such a way as to be free from human error, bias, or manipulation. Both the algorithmic and editorial approaches to knowledge are deeply important and deeply problematic (Gillespie).
I agree that both approaches are deeply important and deeply problematic, because a human can produce an error or a bias while algorithms are designed to automate human judgment (Gillespie). In the case of music streaming platforms, both approaches are being used, so this distinction is of great importance, even though some overlaps are expected.
If we return to the algorithm diagram, we can see that the recommendation algorithms are responsible for content forwarding (i.e. music forwarding in the case of Spotify). This means that these algorithms are mediating the cultural content. As a result, it could be argued algorithms are curating contemporary culture. Other scholars, specifically David Beer, Adrian Mackenzie, Tarleton Gillespie, etc., have already discussed digital culture curation; however, the case of Spotify has thus far remained understudied. For example, the well-known digital video platform Netflix have frequently been discussed among academics, due to its well-known recommendation algorithm (Gillespie 9; Beer Popular Culture and New Media 36; Hallinan and Striphas 1). Furthermore, one of the most known examples in music is the recommendation algorithm of the platform Last.fm, which has been discussed a number of times (Beer Popular Culture and New Media, 53; Beer Power through the Algorithm?, 996). In the book Popular Culture and New Media, David Beer developed the concept that recommendation algorithms can be understood as a way of shaping taste and of circulating the means of popular culture because they are suggesting what the users should pay attention to (86). This attention created by recommendations can be understood as curation and, because algorithms are responsible for taste shaping, this indicates the
15 power they hold. The power possessed by algorithms had been discussed by David Beer, when he presented how Last.fm’s recommendations enhanced post-hegemonic power (Beer, Power through the Algorithm? 997). His thesis was grounded on Scott Lash’s concept of “post-hegemonic power”, which argues that we currently live in the post-hegemonic era in which power is not coming from the institutions and their regime of representation, but it is influencing the society from the inside, in our case from software and algorithms (Lash 75).
1.6. RESEARCH QUESTION
In this thesis, I will present the algorithm behind Spotify’s Featured Playlists in depth and create a terminology to name and describe the processes that this kind of algorithm generates. Developing this theory will be my primary contribution to the field of software studies. With the use of empirical research, I will present how Featured Playlists are functioning and, as a secondary objective, I am will present a method of how to analyse music recommendations within Spotify. The results of this research will be relevant for algorithm researchers and for stakeholders of music streaming services: users, developers, artists and music industry representatives.
I will answer my primary question: What are the requirements to recognize an algorithm that belongs to the group of context-curated algorithms? Furthermore, I will answer the following question: What is the relation between algorithmic and editorial suggestions within the recommendation algorithms? As the final aim of the research, I will suggest how it would be possible to avoid the implications of authority within music recommendation systems.
1.7. OVERVIEW
In the introduction chapter of the thesis, I have presented my object of study. I have also introduced the theoretical framework to be used along with a brief history and presented the aims of the research. In the second chapter, I will introduce all major theories connected with the software and algorithms regarding how recommendation algorithms function. The following chapter will present the research methodology, specifically how the descriptive analysis and empirical analysis will be conducted. The fourth chapter will be focused on Spotify. I will present all research results of both descriptive and empirical
16 research and define suggested terminology to describe this kind of algorithmic behaviour. In the fifth chapter, I will summarize all research results and create a comparison of algorithmic and non-algorithmic curation with examples of present and past. The sixth chapter will be the conclusion of this thesis, where I will answer my research questions, present new possibilities for software curation and express my concerns regarding future software developments.
17 2. LOOKING INTO THE SOFTWARE
Currently, software is involved in many aspects of our daily life, and digitalization could be considered as significant as the invention of combustion engine or the harnessing of electricity (Manovich, Software Takes Command 8). It has a profound influence on society that needs to be examined. Here, I focus on music streaming services and examine one of the main features of Spotify platform. From the perspective of software studies, Spotify is a cultural software since it enables access to cultural artefacts (Manovich, Software Takes Command 20). Furthermore, the service is also a part of a subset, media software (Manovich, Software Takes Command 24). Spotify is one of many cultural programs used on a daily basis; in response to the increasing usage of such software, Manovich explained that:
[…] our contemporary society can be characterized as a software society and our culture can be justifiably called a software culture—because today software plays a central role in shaping both the material elements and many of the immaterial structures that together make up “culture.” (Manovich, Software Takes Command 33)
Software functions as an interface for users to use the data, but content management is done with the use of algorithms. In other words, algorithmic processes are ordering and sorting cultural content and deciding what is important for users (Beer, Popular Culture and New Media 64; Manovich, Software Takes Command 33). The effects of algorithms that are managing this kind of content have been described as algorithmic culture, firstly by Alexander Galloway and later by Ted Striphas (Galloway 18; Striphas). As a result, I am focusing on algorithms in this chapter in order to present how algorithms are functioning and what kind of influence they possess. In the second part of the chapter, I narrow my focus to selected recommendation algorithms in use by the music streaming service Spotify. However, first we need to understand how they function as a whole.
2.1. THE ERA OF ALGORITHMS
As I presented in the introduction, I am looking at algorithms on three levels. The first level is a wide one that combines different types of algorithms that are later more specified by their function. In general, they are functioning as software engines, and one could argue that they are functioning purely automatically. However, in some cases, they are subjected to the influence of external sources.
18 This has to be considered from two different angles. On one hand, we have to acknowledge the so-called “algorithmic objectivity” that is concerned with how algorithms are being developed and, on the other hand, the perspective in which algorithms already are functioning, but the input is being edited by human entities, in this thesis also referred to as “editors” (Gillespie). Looking at the algorithms from the user perspective, they can be seen as completely fair apparatuses “free from subjectivity or error” (Gillespie). However, Gillespie explains that this is not always a fact:
More than mere tools, algorithms are also stabilizers of trust, practical and symbolic assurances that their evaluations are fair and accurate, free from subjectivity, error, or attempted influence. But, though algorithms may appear to be automatic and untarnished by the interventions of their providers, this is a carefully crafted fiction. (Gillespie)
A similar theory was also created by Mackenzie, claiming that the primary issue within the algorithms is that the order that they create looks too natural and unmistakable (Mackenzie, 63). Just as editors of the traditional media have established the moral ethics of the content creation, Gillespie does so for algorithms. Here, the developers are addressed. Algorithmic objectivity is a part of a bigger debate about whether genuine objectivity is even possible; Evgeny Morozov claims that algorithmic objectivity, or with his terms, “neutrality” cannot ever be fully reached (145). To some extent, Nick Saver is also included in this debate in Knowing Algorithms in which he acknowledges the power of algorithms, saying that the solution to create algorithm objective is to make them transparent;
The solution is transparency: filters and the content they hide should be made visible (Savers 3).
This is a general statement, although recommendation algorithms of social music platforms can be used as an example. Because it is music they are curating, users would expect that the algorithms do this completely objectively; however, as I mentioned in the previous paragraph, that might not be the case.
Furthermore, we have to acknowledge another possible influence that affects the algorithmic output: editors. Content, as an input to an algorithm, can already be chosen by human entities, creating another level at which the output can be influenced. Gillespie also proposed this possibility; however, he stated that this can also be sometimes positive (Gillespie). I tend to agree that expert knowledge can be a welcome benefit to content curation, although the sense of authority cannot be avoided. Here, we can see a double influence that can be applied to the algorithmic mediation of (cultural) content. This means
19 that the outputs of the algorithms are generally subjected to a number of filtering layers before they reach the user.
The filtering processes within most algorithms are not transparent, and it could be argued that as a consequence algorithms possess influential power to its users. In general the proclamation of power within algorithms, has been acknowledged by Hamilton et al. and Scott Lash (Lash 75; Hamilton et al.). Here again we can take an example of recommendation algorithms for cloud music services. Recommendation algorithms possess power in a sense that they are suggesting music to their users and affecting their cultural taste (Beer, Power through the Algorithm? 997). The power they possess is, however, not the same as power over somebody, but in the way of shaping user experiences (Beer, Power through the Algorithm? 997; Beer, Popular Culture and New Media 63).
In order to bypass this power within algorithms, we have to acknowledge what Saver suggested. His solution for lowering the power is by uncovering and making algorithms transparent (Savers 7). However, this might not be as easy as uncovering the code of their systems, since they are trade secrets (Savers 7). Here, I agree with Saver, a complete revelation of the code would be unacceptable for corporate entities. However, I am endorsing empowering users with the possibilities to edit the code. This would result in much lower levels of the power that we see now in algorithms.
The increasing importance of the power that is generated by the algorithms is not merely a discussion for digital technology researchers, media experts or developers. The power invested in music streaming platforms has also been recognized by the music industry. In the IFPI digital music report for 2014, they presented the statement of interest in the algorithmic recommendation within the cloud music services. For some time, the competition between them was based on the volume of music offered; however, this has now shifted to recommendations and music discovery (“Digital Music Report — IFPI — Representing the Recording Industry Worldwide”). Of course, both are connected to the music offered by platforms, but currently all of them are offering vast databases of content. This means that the competition between the services has shifted to quality of the algorithms.
20 2.2. RECOMMENDATION ALGORITHMS OF STREAMING MUSIC SERVICES
To this point of the thesis, I have been discussing algorithms in general and presented some of the general algorithmic features with examples of recommendation algorithms, since they are my case study. From this point onward, I am concentrating only on the second level of the previously introduced algorithmic diagram. This level is concerned with recommendation algorithms; however, I focus only on those used within the music streaming services. As presented beforehand, these recommendation algorithms are highly valuable for the music industry.
As already mentioned, some of the algorithmic processes are hidden and are shaping individual taste and affecting the culture in general. In the case of music streaming platforms, recommendation processes are resulting in hidden cultural connections. Arguably, they are regulating music consumption within the platforms. The significance of this cultural influence grows simultaneously with the growth of services as well as users engaged in the process. According to Beer:
The influence that this [cultural connection] has on the cultural landscape of individuals can only be significant when we imagine the scale of the use of such systems, and when we begin to add together some of the most prominent means of cultural consumption in iTunes, Amazon and the like. (Beer, Popular Culture and New Media 95)
Moreover, here I would like to extend Beer’s collection of online cultural consumption platforms and sites to streaming platforms, such as Spotify, Deezer, Apple Music and others. These services are experiencing a rising numbers of users engaged. Based on the digital music report for 2013, 61% of the Internet users are using licensed digital music services (“Digital Music Report — IFPI — Representing the Recording Industry Worldwide”). This can be also understood as an indication that the influence of algorithm empowered platforms is on the rise. As such, we can see music streaming services as a part of an infrastructure that possesses the power to shape tastes. I agree with Beer in his statement that currently there is a bigger influence coming from new media than social interactions:
[…] not to say that social class and personal networks do not shape taste anymore, but that ultimately we may find in new media infrastructures powerful forces that implicate the direction of cultural tastes. (Beer, Popular Culture and New Media, 91)
Algorithms not only direct cultural tastes, but for the first time tastes have also become metricized. David Beer explains this as well in his book Popular Culture and New Media, in which he claims that the usage of modern software has opened possibilities to gather user-specific data and made it possible to to metricize the cultural taste of individuals
21 for the first time (Beer, Popular Culture and New Media 63). This can result in the creation of accurate societal cultural patterns. Hamilton, Sandvig, Karahalios, and Eslami, in their article A Path to Understanding the Effects of Algorithm Awareness, describe algorithmic visibility. They suggest different approaches for exposing the work of algorithms by “unblackboxing” the processes (Hamilton et al.). Here I suggest an expansion of their idea, not only to unblackbox the processes but also to look for algorithmic side products, such as metricizing cultural taste.
If we return to the power invested in recommendation algorithms and the shaping of users’ tastes within music streaming services, we can see that these algorithms are curating the content. Within the music streaming platforms, the algorithms that are curating the content have significant responsibility:
Francis Keeling, global head of digital business, Universal Music Group, says: “To fully engage users, services need to provide a well-curated experience. Music fans love to discover new music, and digital services need to be experts at music recommendation.” (“Digital Music Report — IFPI — Representing the Recording Industry Worldwide”)
As a result, platforms are currently researching how to improve this curation or recommendation algorithms in order to provide a better experience. In the case of Spotify, one of the updates happened at the end of May 2015.
2.3. SPOTIFY’S RECOMMENDATION ALGORITHMS
Looking more specifically into the second level of the algorithmic diagram, there are a number of recommendation algorithms currently in use within Spotify. I will look into older concepts of algorithms that remain in use and introduce new developments. A number of online platforms are still using the “traditional” recommendation systems, including Spotify, Last.fm, Amazon and others, which use a function based on algorithmic calculation, named “collaborative filtering” and further explained later in this chapter (Johnson; Lee, Yeo, and Lee). However, in summer 2014, Spotify decided to develop their services with a music intelligence platform that synthesizes billions of data points based on different algorithmic calculations, called the Echo Nest (“The Echo Nest Joins Spotify!”). With Echo Nest’s database, Spotify not only gained an extensive catalogue of songs, but also an analysing tool that functions on different principles that collaborative filtering does. The Echo Nest’s algorithm analyses audio content, files’ metadata information and lyrics, while collaborative filtering functions with the use of a method that collects a feedback from users that are listening to similar songs (Celma 61; Johnson). As shown in Erik Bernhardsson’s
22 presentation, the Spotify team wants to combine more signals, such as the Echo Nest’s content-based and collaborative filtering methods, in order to improve recommendations and to personalize all the functionalities within the platform (Bernhardsson, “Music Recommendations @ MLConf 2014.”). Josh Constine already introduced the first peaks of new combined algorithm called “Truffle Pig”; however, it is only available for the employees and not yet ready to be incorporated into the platform (“Inside The Spotify – Echo Nest Skunkworks”). This is a renewed concept of the personalization system developed by Spotify. While Spotify is working on new personalization techniques and combinations for new models of recommendations with the usage of machines, their competition Pandora, another cloud music platform, is continuing to deliver music recommendations to their users with the Music Genome Project.
Each song in the Music Genome Project is analyzed using up to 450 distinct musical characteristics by a trained music analyst. These attributes capture not only the musical identity of a song, but also the many significant qualities that are relevant to understanding the musical preferences of listeners. (“About The Music Genome Project®”)
Despite the fact that Pandora uses music professionals to tag music, or that Spotify is developing a new algorithm, the recommendations are still linked between the database and the user with the usage of algorithms, making The Music Genome Project and Truffle Pig algorithms just different approaches to algorithmic culture. By this I mean, that even if music professionals or human entities prepare the inputs of the algorithm, the output will still be algorithmically managed, and this will still result in algorithmically shaped tastes. Looking more closely at the recommendation algorithms or the second level of the algorithmic diagram, I build my case on two examples. First, I will introduce the algorithm that is specifically intended to address the users with what they want, based on their listening history and secondly I will address the context that on which the recommendation is based.
2.3.1. PERSONALIZATION ALGORITHM To this point in the thesis, I have introduced general views on algorithms and presented what kind of algorithms are in a general use with Spotify. As previously stated, I am concerned with recommendation algorithms in general; however, two of them of interest for this research. In the following two chapters, I will theoretically introduce two of Spotify’s algorithms in greater detail. The first is the personalization algorithm called “Discover”; the second is the “Featured Playlists” algorithm. These two algorithms are a part of the third level of the algorithm diagram.
23 When discussing personalization, we cannot think only about an upgrade of the general recommendation systems, but more about a new functionality empowered by an algorithm to satisfy each user individually. Although this algorithm has become a separate part of the topic, it remains a part of the recommendation branch. In general, it filters the content for each user individually, based on their previous actions. This branch of Internet filtering is not new, but it has become more influential only in recent years, as is increasingly embedded into everyday systems.
FIGURE 7 - SPOTIFY DISCOVER FUNCTIONALITY
Spotify currently delivers its personalized stream only through the functionality called Discover (Figure 2). However, Spotify developers aim is to embed personalization even further into the software, not only within a specific functionality but throughout the software (Bernhardsson, “Music Recommendations @ MLConf 2014”). As previously stated, personalization algorithms follows individual users‘ behaviour and, based on their past actions, they calculate what one could be interested in. These types of algorithms have triggered a number of debates. When I am writing about personalization, I am looking at them from two different standpoints. On one hand, I am considering Eli Pariser’s thesis of personalization as an instant loop inside of a so-called “filter bubble” (Pariser, The Filter Bubble 9). He defines the term as:
[…] these engines [personalization] create a unique universe of information for each of us – what I’ve come to call a filter bubble- which fundamentally alters the way we encounter ideas and information (Pariser, The Filter Bubble 9).
In other words, once users start to use these personalized services they become trapped within a filtered environment of closely selected information, prepared strictly for that
24 individual. On the other hand, Ted Striphas’ perspective is not fully opposed to Pariser’s, but he does present a different perspective on personalized algorithms. He argues that with the help of algorithmic systems we are able to be introduced to the cultural goods that we might not have encountered otherwise (“Culture Now Has Two Audiences”). This means that because of the filtering provided by personalization, we are able to obtain much more relevant information for us than at any time before. In this sense, personalization brings us something that no other system could: a set of information strictly prepared for each individual.
For an example, I return to Spotify. The software currently has personalization embedded into one particular functionality, and if users wish to see what the algorithm is suggesting to them, they can voluntarily open the tab and browse through the results. This is the positive effect of personalization that Striphas described, i.e. that the user is capable of obtaining information that they otherwise could not. However, as I mentioned before, since Spotify developers’ aim is to personalize everything, the results might be more similar to Pariser’s idea of a filter bubble. An example that could be taken into consideration is the Truffle Pig algorithm. Based on the TechCrunch article, we can see that the developers, Spotify employees, the test users and the journalists researching it have high expectations regarding this update (“Inside The Spotify – Echo Nest Skunkworks”). Despite their positive attitude towards the updates, I have my doubts about the updated personalization. To explain this, I return to Pariser.
First, to understand this, I will use Pariser’s explanation of the basic model of personalization. In order for algorithms to provide us with a set of personalized results, they first have to determine what people like (Pariser, The Filter Bubble 112). Only after that are they able to provide them with the content that fits their tastes, following which they fine- tune the settings as the users continue to use it (Pariser, The Filter Bubble 112). As much as this sounds promising for the users, it also raises concerns. With algorithms like these, the user’s identity is being shaped without him/her noticing (Pariser, The Filter Bubble 112). This is reminiscent of Beer’s idea of shaping tastes, but he talked about algorithms, in general, whereas here we are focusing only on personalized results.
Pariser’s theory about filter bubbles (as previously introduced) claims that personalization encloses users into individual clusters, i.e. a unique cultural universe. This means that users lack the possibilities of free content exploration, content novelties, and serendipity. In Pariser’s TED talk, he addresses the fact that for a period of time the Internet
25 offered this kind of exploration possible; however, over time the Internet became an enclosed space due to regulations on the content flow (Pariser, "Beware Online 'Filter Bubbles.'"). Returning to the case of Spotify, their extensive database makes personalization the logical next step in the development; however, the main problem here is how this will affect the user results.
As an example, I am going to present one of my personal experiences. I am not a big fan of mainstream music, although I like to follow some trends, so I have a something to talk about with my friends or someone that I just met. Usually, I am listening to my own playlists on Spotify, with music selected for my personal taste. My playlists are usually five hours long, and every song is from a different artist. In my opinion, my taste is not the most conventional, but nevertheless Spotify’s personalization algorithm will be able to suggest me more music like that if I will access the Discover tab. At the same time, I can still click Spotify’s New Releases tab and see the newest mainstream releases. However, if Spotify will continue with their plan to personalize everything, I might be able to see more of the music I like but will be cut out of the mainstream feed. On one hand, I will like it a lot, since I will be able to listen to more music that I like; at the same time, I will be enclosed within my small music world. It could be argued that this is one great advantage that we have with the Internet, as in the past I would be subjected to the broadcast media and the selection of their editors. Pariser also addressed this topic as well, saying that:
[…] in a broadcast society, these gatekeepers, the editors, and they controlled the flows of information and along came the Internet and it swept them out of the way, and it allowed all of us to connect together, and it was awesome. (Pariser, "Beware Online 'Filter Bubbles.'")
However, the situation is not that bright. We have exchanged the human gatekeepers for algorithmic ones, which do not even have the same embedded entertainment media ethics as the editors did. This recalls Gillespie’s view of algorithmic objectivity, in which an algorithm should be structured similarly to the norm of objectivity in Western journalism or entertainment media ethics (Gillespie). Consequently, personalization should be made in such a way that it does not limit users and offers them only a small proportion of the culture/music but in a sense that it would connect very specific information, selected for each user individually while simultaneously offering them with some unrelated content that would add serendipity and novelty to their results (Pariser, "Beware Online 'Filter Bubbles.'").
26 As stated beforehand, Spotify is for now still clearly separating personalized suggestions from others. The Discover algorithm is suggesting music based on the user’s listening history, and this kind of personalization is not a matter of concern regarding filter bubbles as what are personalized results and what are not are clearly stated. As Spotify plans to personalize everything, this could occur with further development. An interesting point here is that personalization promises to bring individual results to each user, although it functions based on collaborative filtering. Here I am referring to Spotify and to other music services (Bernhardsson, “Collaborative Filtering at Spotify”). Collaborative filtering defines connections between songs and artists, it then tracks user’s listening and matches them with similar users (Celma and Lamere 61). The results that the user gets are the connections with songs/artists that users from the group listened to, and the user did not (Celma and Lamere 61). This creates a contradiction of personalization, behind which is the promise of satisfying each taste individually. In reality, we can see that each user is being put into a group of listeners and then matched with what they listen to. This is understandable, because collaborative filtering is not producing a purely individual set of results, but group ones.
On the same algorithmic level as personalization is the Featured Playlists algorithm, which could be considered to be the opposite of personalization. By this, I mean that the personalization algorithm is based on a filter that recommends music to users based on their listening habits, while Featured Playlists does not take it into account. Featured Playlists recommend music based on the context in which the user is engaged.
2.3.2. CONTEXT ALGORITHM On Spotify, we can find another specific recommendation algorithm that is being used to curate the music based on the current moment or context of the day. This functionality, which I introduced before, is named “Featured Playlists”. Because this section has no title within the software I am using, the name “Featured Playlists” is taken from the developer’s web page for Spotify. In this thesis, I use this term to describe both the feature and the algorithm that powers it. It is stationed on the Overview tab of Spotify, and I am using it as an example on how digital recommendations can be inspired by traditional media. An example is shown in Figure 3.
27
FIGURE 8 – FEATURED PLAYLISTS SECTION
In the case of Featured Playlists, Spotify offers two types of recommendations simultaneously. The first are the algorithmic suggestions, and the second are the editorial suggestions. Algorithmic suggestions are responsible for fitting playlists to a specific mood at the right time (this will be proven in the following chapters of this thesis). Editorial suggestions, in contrast, employ music experts that create playlists that fit a specific mood. Their creations are playlists that offer a number of songs that should satisfy user’s specific mood, type of music or music within a specific subculture, (e.g. Beautiful Day, Viral Hits). The type of satisfaction that I have in mind here is connected with the logic of traditional broadcast radio. Jody Berland sees the radio music selector as a narrator, while I prefer to use the term “editor”. She explains their role on the broadcast radio as:
[…] the DJ or radio host a kind of narrator, and suggests that the combined elements of new and old songs, advertisements, news and weather on the hour, and so on, can be analyzed as structural functions within the narrative, which is constructed through their specific combination (Berland 188).
The inclusion of this narration, which was once the task of radio personnel, has now shifted to algorithms, at least in the case of music streaming platforms. Of course not all specifications can be applied solely by algorithms; some are managed with user’s knowledge. Here I am talking about music curation and not about the content (such as news etc.). In the case of Spotify, music editors can be regular users, music industry representatives or curators employed by the platform itself (Figure 4).
28
FIGURE 9 –PLAYLIST CREATORS
However, only platform curators, such as the users of Spotify and Spotify Netherlands, are preparing playlists that are forwarded to the users by Featured Playlist algorithm. They select music that would satisfy listener’s expectations in the particular moment in their opinion. In this thesis, I am using the term “access momentum” to describe the particular moment in which a user accesses the software, and the algorithm produces a set of results based on their context. This means that Featured Playlists are functioning based on the so- called access momentum in which the algorithm acknowledges the time and day of the access and presents editorially prepared playlists for that particular moment. This describes what I call “context curation”, which a set of results prepared for the moment of access linked by the algorithm.
To summarize, recommendation, personalization, and the featured playlist algorithm pose a number of considerations. One of the concerns of this thesis is the subject of algorithmic objectivity, as they have the power to reshape the popular culture. Arguably, the algorithmic processes applied to the software have an influence on the end users that is greater than that of traditional media. In this case, society might face a cultural reshaping due to the algorithms in use, which is, of course, not only due to Spotify’s recommendation algorithm but also due to the power and influence of the software that users engage with in general. Focusing only on culture, David Beer had a similar thought. He stated that with the use of predictive processes and the significant power that they possess, they are shaping and will continue to shape users’ encounters with culture. (Beer, Popular Culture and New Media 97). However, if we focus again on the case of music suggestions, the algorithmic embrace of consumer culture will increase users’ cultural knowledge. (Beer, Popular Culture and New Media 97) In total, due to the high embeddedness of algorithms in our life:
29 […] today’s culture may only be as good as its algorithms. (“Algorithmic Culture. “Culture Now Has Two Audiences”)
This means that as we are subjected to algorithmic choices on such a high level, we have to ensure that the algorithms that are suggesting us what is worth paying attention to are as good as possible. As a result, I also decided to explore the Featured Playlists algorithm in depth. In the following chapter, I present the methodology I used to research it.
30 3. METHODOLOGY
The primary aim of this thesis is to analyse the work of algorithms within Spotify. In particular, I am interested in how music is curated on Spotify. In the case study of the Featured Playlists algorithm, I investigate context-based curatorship and analyse the relation between algorithmic and editorial suggestions. In order to describe its functionality, I establish a term that was already mentioned in the introduction, context curation and use it to describe the produced results. My research is based on interface analysis and the investigation of algorithms. In order to present this, I performed front-end and back-end analysis of the software. By “front-end”, I refer to the part of the software available to users, what they see and how they can interact with it. With the term “back-end”, I describe the access to the software from the developers’ side. This kind of research can be used to prove a number of different points. In this thesis, however, I focus on investigating the software as a medium and the algorithm as the engine behind it. This means that I am not exploring the behaviour of software users, nor I am focusing on the content, but I am revealing the communication patterns in use. In order to perform this kind of research, I turned toward the methodology that allows exploration of the software. The interface analysis has been done with the usage of a research account and method described by Mel Stanfill, while the algorithmic analysis has been done on the basis of methods introduced by Sandvig, Hamilton, Karahalios, and Langbort, called “auditing algorithms”, which will be presented in detail.
3.1. INTERFACE ANALYSIS
In order to present the software, I decided to perform an interface analysis through design. This part of the research has been done on the basis of an interface analysis introduced by Mel Stanfill and his idea of observing interface design (Stanfill 1). While his idea of interface analysis was intended for web pages, I will adapt it for use within platforms (Stanfill 2). For the basis of my methodological approach to interface analysis, I will take the “Discursive interface analysis” that examines “affordances of website” (Stanfill 4). In the case of a platform, I propose performing the observations with a research account that enables access to the front-end of the application, and then ‘deconstructing’ it into logical sections. After that, each part of the platform must closely be observed. By “observed”, I
31 mean looking at the design and uncovering all the functionalities that each section has. After that, each functionality has to be tested with the usage of the research account. When dealing with interactive platforms, it is expected that they will respond to our behaviour or to any other influences. Therefore, the testing must be performed until some results show up, or the researcher concludes that the platform does not respond to the user interaction.
For the purposes of this research, I have created a research account. This was done on Spotify’s web page, accessed from the Netherlands. The account was free of charge, and it allowed installing the software on a personal computer. The research account was connected with the software and allowed me to access all functionalities on the interface, for the price of embedded visual and sound based commercials. The observations took place on the opening screen of the software as most of the attention was focused on Featured Playlists functionality, which is placed on the opening screen of the software. However, observations of the software alone were not satisfying in some cases, and I needed to perform some external research, such as web browsing for particular subjects, which are further explained in the next chapter. All Spotify descriptions were made from a personal computer application accessed from the Netherlands.
FIGURE 10 - CREATION OF RESEARCH ACCOUNT
In order to enhance my analysis of the context-curated algorithms within Spotify, I looked into a newly developed feature of Spotify’s mobile application. For this analysis, I did not use my research account, and I did not observe the application in operation, but I did gather information online in order to describe its functionalities.
32 3.2. ALGORITHM ANALYSIS
The algorithmic study was focused only on a particular functionality of the Spotify, Featured Playlist. The data necessary for observation has been gathered with the usage of Spotify’s Application Programming Interface (API). The methodological approaches for conducting research with the APIs were introduced by Lomborh and Bechmann, who described how APIs are primarily used by third-party developers to connect new add-ons to existing services and can also be used for empirical analysis of social media services (Lomborg and Bechmann 256). APIs are the interfaces that enable connecting with the media software (Lomborg and Bechmann 256). A specific computer program can use an API to mine data from social networks (Lomborg and Bechmann 256). As media software, Spotify allows access to some of its data. For this case study, Spotify’s Featured Playlists API for developers was used to gather the data. To enable this function, I connected my research account used for the descriptive analysis with Spotify’s Developer web page. There I was able to create a Spotify application that provided me with a unique Client ID and Client Secret Code, which allowed me to create a connection with the platform. This kind of research method was suggested by Sandvig, Hamilton, Karahalios and Langbort in their article Auditing Algorithms (Sandvig et al. 6). As they explain, the word “audit” originated from Audit Studies that researched discrimination of minorities by simulating applying for a job, requesting a mortgage, or buying a car or using similar services (Sandvig et al. 6). However, with auditing algorithms, they propose researching the algorithmic discrimination and other normative concerns that can be applied in the algorithms of online platforms (Sandvig et al. 6). In their article, they describe a number of different approaches that can be used to investigate algorithms, including automatic and manual approaches that can also be related to Audit Studies (Sandvig et al. 8).
FIGURE 6 - SPOTIFY CONTEXT TARGETING FOR BRANDS
For my case study, I have selected an automatic “scraping audit” approach, suggested by Sandvig et al., which uses an API communication with the platform in order to research the platform, see Figure 6 (Sandvig et al. 12). Scraping, in this case, is an approach that enables to gather posted content accessible to the users.
33
If I focus back on Featured Playlists, I am concerned with Spotify’s objectivity and with the power that Featured Algorithm possesses in shaping user’s cultural tastes. With the usage of the scraping audit principles, I mined for the data, presented by the Featured Playlist algorithm. I use the term “mining for data” to describe the transfer of the information from the back end of the platform to researcher’s data drive. In order to perform these tasks, new research software was developed that required a number of repeated queries to mine and to save the data. The code of the software can be found in Appendix 1. The results were later manually observed; however. due to an extensive amount of data mined only a smaller pattern of the data was examined. The pattern was chosen on the grounding of the Spotify’s playlist targeting plan (“Spotify Launches Playlist Targeting for Brands”). In other words, to brands Spotify offers to target the listeners based on the context in which they are (Figure 7).
FIGURE 7 - SPOTIFY CONTEXT TARGETING FOR BRANDS
For the particular case study, the Featured Playlist API has been used to scrape the data from the platform with the usage of Spotify Developers API (“Spotify Web API Console”). This technique bypasses the user’s point of view and accesses data directly from the database, and it can be described also as a back-end observation (Sandvig et al.). The research software was developed with the usage of the Python coding language and was based on two files, pulldata.py and scrapandsave.py. The pulldata.py file was used to create the communication with Spotify. With the usage of developer identification code, the software was connected with the platform through the registered application on the Spotify for Developers web page (Figure 8).
34 FIGURE 8 - SPOTIFY DEVELOPER APPLICATION
In this file, what kind of data the software should mine for was also identified. The research software here called for the country-specific register of Featured Playlists. The second file was used to request the platform for the data. When new data was available, this file transferred and saved it. In order to save the data, the software firstly generated country specific folders, created day-specific folders, and then each set of time data was transferred; it then created an .html file containing the mined information with a unique name defining the time specificity, see Figure 9. More particularly, the research software generated server folders, Root / Country / Day abbreviation Date Month 2030/ and created a new.html file that was named by Country code – Day Month Date Hour-Minute (example http://5.152.178.115/HK/Thu%20Apr%2030/BR%20-%20Mon%20Apr%2027%2000- 30.html). In these files, the software saved a list of names of Featured Playlists that appeared on the user’s screen at that moment.
FIGURE 9 - RESEARCH SERVER ROOT DIRECTORY
The research software was installed on a Virtual Private Server (VPS), m a Linux container server with OS Ubuntu 14.04-LTS. The VPS server was based in the Netherlands, and it was functioning based on the Central European time zone, see Figure 9. In order for
35 the research software to function, a Python3 software was installed and three external data libraries (Jinja2, Spotipy, and Request). These three libraries are needed in order to scrape Spotify’s data (“Spotify Web API Console”). The server started functioning on the 24th of April 2015 at 09:05:03 (Central European Time), and the script started to mine the data on Monday, the 27th of April 2015 at 00:00:00 (UTC+1). In the following chapters, the research software will be also referred to as the script.
FIGURE 10 - VIRTUAL PRIVATE SERVER (VPS)
The script was set to gather the data that Featured Playlists had offered on Spotify for 8 days, from Monday, the 27th of April until Monday, the 4th of May 2015. The script was set to mine data from one country per continent where Spotify is available. The decision about which countries should be studied was made based on a number of considerations. I chose the Netherlands as the representative of Europe, the United States of America as the representative of North America, Brazil for South America, Australia, and Hong Kong as the representative of Asia. Brazil and the United States of America were chosen for this research because they are the most populous countries on the continent that they represent. Australia was selected as the only available option on that continent, and the Hong Kong Special Administrative Region of the People’s Republic of China due to the combination of Chinese and Western cultures. Antarctica and Africa were excluded from the research since Spotify is not available on those two continents. In May 2014, Spotify stated that it was going to offer its services in the Republic of South Africa; however at this time the services there remain unavailable. All mined data is available in Appendix 2.
Although an extensive amount of data was mined from Spotify, I decided to use the dataset from the Netherlands as the main dataset. Furthermore, I decided to use another dataset to verify culture-specific content. This was the dataset from the United States on the 27th of April. How exactly this dataset was used is explained later in this chapter. I chose this dataset since the research is being done in this country, and most of the conclusions are based on this data only. All the scraped results were put into an Excel table, in which every
36 row represented the time at which the data was gathered, and every column showed the results gathered in one day. Due to the extensive amount of data mined in eight days, I chose to observe seven specific times on the day the curation had been done, based on the pattern introduced by Spotify, in which they presented the seven main contexts in which the listeners engage with the playlist (“Spotify Launches Playlist Targeting for Brands”). In this case, these contexts have been used to offer target marketing for brands. The times when the data was observed were 06:00, 08:00, 15:00, 17:00, 19:00, 21:00 and 23:00, see Figure 6. For a better distinction between the playlists, I grouped them into classes of activities and moods, again based on the Spotify for Business targeting preferences, namely: Sleep, Focus, Mood Playlists, Workout, Dining, Romance, Commute/Travel, Party, Chill and Getting Ready. (“Muziek Voor Iedereen.”) In order to address some more specific playlists, I added two categories especially used to address the day-specific playlists and culture-specific playlists. The day-specific playlists were those that were meant to be played on a particular day. In some cases, the playlists could be a part of more than just one context, but in order to make a more specific case study, I selected only one group per each playlist in which the most representative context was chosen as the primary one. The culture-specific playlists are focused on the particular cultural subjects. Additionally, I needed to add a genre category for the playlist that did not address a specific mood, but a specific genre. This part of the research was done manually using the front-end observation to categorize the playlists correctly. Playlists were color-coded inside the Excel software (see Figure 11).
FIGURE 11 - COLOR-CODED DATA IN EXCEL
37 With the usage of this color-coded data, I focused on contexts and on how Spotify curates music with the usage of playlists in general. In other words, in this research I compare the overlaps of the playlists offered by the algorithm between the days. In the second part, I focused on a comparison of the results between the days for culture-specific cases. In order to address cultural-specific dates in a dataset of the Netherlands, I used the United States dataset of the same day to make the comparison.
3.3. LIMITATIONS
In conducting this research, I encountered a number of limitations. First, it was expected that every day the script would gather the Featured playlists at least twenty-four times, once an hour; however, after the resulting mining was done, it was clear that the featured playlists are posted in different times the day. In some cases, the data was gathered every hour, while in other cases it was updated more than once per hour, resulting in overlapping results; sometimes there was a few hours gap before the next update was posted. There was one instance of the results being corrupted due to the research software and once due to the hosting services of the VPS. The gap that occurred due to the research software was on Monday, the 27th of April between 00:30, when the last update was saved, 11:02 of the same day, when the next update was saved. From that point on, the research software did not produce any other issues. Due to the VPS hosting service, a gap appeared on the May the 2nd from 21:00 to 00:00 due to the maintenance on all of the servers (see Figure 12). No other gaps in the data mining occurred. The second main limitation that I came across with was the problem of data comparison between the countries due to the different time zones. This happened because the data did not have any reference points for which the times could be synchronized. The only sample that I needed to link with my data was the dataset of the 27th of April from the US. In that case, I did not compare the data based on time, but I used the whole day as a reference.
FIGURE 12 - MAINTENANCE ANNOUNCEMENT
38 4. CONTEXT AND CURATION
As I described in the previous chapter, I have observed a specific part of the Spotify platform in depth, from both the front-end and the back-end. Combining the two brings us closer to the primary question: To what extent do algorithms promote context-based content on media platforms? I observed the gathered data and identified patterns of curation. Furthermore, I was able to classify them due to the front-end examination. In this chapter, I discuss the user experience offered through the Featured Playlists of Spotify. Here, I focus on the observations of playlist functionalities, patterns of context, algorithmic suggestions and make observations about the editorial curation.
4.1. FRONT-END INTERFACE ANALYSIS
Before moving specifically into the descriptive analysis, I have to explain how the content is managed within the software. When managing a catalogue that is as big as Spotify’s music catalogue, how to navigate through the data is of great importance. This applies to the navigation through the data from users’ side, as well as navigation from the perspective of system administrators. In the core of the platform are music files; however, one of the most important specifications of Spotify is their database taxonomy. When I use the term “taxonomy”, I am referring to the practice of logical classification and categorization. In this thesis, I consider the logical categorization of music clustered in groups. Spotify’s primary technique to structuring the music is to merge music into playlists. As a result of this structure, Spotify opened more options for its users than its competition services.
By using playlists, the platform opens a unique principle of music delivery that can also be described as music curation. In this manner, music is merged into clusters of songs, and they share at least one specification, such as genre, beats per minute (bpm) rate, etc. It does not make any difference if a user listens to a song from a specific album, or to a song of a certain artist: in both cases, they are accessed through a playlist. Additionally, the playlists are created in order to cluster songs and tag them with the feeling that a specific playlist offers (Haupt 134). The playlists are the only way to save the music for further listening; users can create a new playlist on their own, or save a track on the “to listen later” playlist. Any user on the platform is allowed to do this; they can be a part of administrator team, music industry representative, a paying user or a free user (Figure 4). The creation of
39 playlists by all three entities can provide a foundation for music curation inside the platform. If users are interested in any playlists, they can follow them, and they will be informed about the updates within them. With this, they become subscribed to the professionally curated, celebrity curated or other lists of music (Haupt 134). The main reason Spotify itself has to participate in the creation of the playlists is to keep the platform interesting for the users at all times. This will be further discussed in Chapter 5. The creation of these playlists is an exemplary case of editorial recommendation. As previously presented, all participants can be editors within Spotify; however, which playlists are being promoted and which are not is an algorithmic matter, presented in the second part of this chapter. Next, I move from the topic of the playlists themselves and describe Spotify in general, how the dashboard looks and what the main functionalities are.
FIGURE 13 - SPOTIFY OPENING SCREEN
As seen in Figure 13, Spotify’s dashboard is split into three main columns. They combine some of the software functionalities on the left side of the screen, provide an overview of friends in the column on the right and in the middle is the content part. The music player is located on the lower left edge of the application. The tab, which is the primary focus of this thesis, is the Browse window, open in the Figure 13 and Figure 14. In it, the user is able to navigate through an extensive database of featured and non-featured content. Within the Browse window, users can choose different types of curation. What we can consider to be curation made by Spotify are the sub-tabs of the Browse page where
40 Spotify currently offers Chart playlists, Genres and Moods playlists, New Releases and News pages. The last option of curation is the Discover tab. Only two of the tabs in Spotify’s Browse section are not directly connected with playlists. The first one is the News section, which offers recent news about artists and is based on written articles; the second one is the Discover section, which is powered by the suggestion algorithm for personalized music recommendations. In the end, both of them still offer playlists. News link to the artists and the Discover function suggests artist playlists. The results of Discover functionality are generated based on the user’s listening history.
Discover has music recommendations tailored just for you. (“Discover - Learn more/Guides - Spotify”).
On the Discover page, users can find artist recommendations based on the overview of their listening history and recommendations based on the links between particular artists that the user was listening to (see Figure 14). The Discover algorithm also powers the Radio functionality, which is based on creating a stream of songs from similar artists (Bernhardsson, “Music Recommendations @ MLConf 2014”).
FIGURE 14 - DISCOVER WITHOUT USER’S LISTENING HISTORY
41 4.1.1. FEATURED PLAYLISTS ANALYSIS For our research, the most interesting part of the Browse page is the opening page or the Overview section, since the Featured Playlists are located there (see Figure 11). The Featured Playlists is one of the parts of the overview window that is always visible. Every time the user opens the software and every time user returns to the “beginning”, they are faced with this functionality that should satisfy the user’s experience for the current moment. See Figures 2 and 12. Figures 3 and 13 were taken on the same day; the first screenshot was Figure 12 taken at 17:04 (Screen Shot 2015-04-19 at 17.04.35) and Figure 12 with just a few seconds delay, with only a few seconds still at 17:04 (Screen Shot 2015-04-19 at 17.04.37). What we can see here is that the content offered to me was context based. April 19, 2015, was a Sunday, and the Featured Playlists algorithm offered me context- curated music playlists. I take this as the first example of music recommendation that can be described with my term “context-curated recommendations”. The Featured Playlists algorithm can only be considered to be one of the algorithms that are functioning as context-curated recommendations. During the writing of this thesis, Spotify released an update that is even more specifically connected with the context. The Featured Playlists algorithm was pushed forward, i.e. promoted more on the mobile application; they also released a new mobile feature, Running, which I will describe in greater detail later in this chapter (“New on Spotify.”).
FIGURE 15 - BROWSING THROUGH SIMILAR ARTISTS
Spotify, of course, also offers streaming of music on-demand and lets the user choose which artists they want to listen to. Furthermore, as previously mentioned, Spotify offers a functionality of radio streaming based on similar artists and a functionality that
42 offers users the possibility of browsing through the similar artists manually, as shown in Figure 15. Connections between the artists are made with the usage of collaborative filtering (Celma and Lamere 61). Recommendations based on collaborative filtering and similar functionalities are an interesting part of recommendations; however, due to the limitations of this thesis, I am focusing only on context related functionalities.
This front-end observation of Featured Playlists gave us the first example of context- curated recommendations. From this point onward, I analyse Featured Playlists in greater depth, connecting them with the notion of editorial curation, presented within the section of playlists.
Featured Playlists always appear with a general subtitle that addresses the access momentum, using the day and time logic. For instance, on Sunday morning, I encountered the subtitle “Kruimels in bed” (see Figure 16). With further investigation, I identified four levels of referring to the context used. The first level is changing the title of Featured Playlists, mentioned previously. The second level will be addressed later in this chapter.The third level includes titles of playlists that were taken in greater observation in the back-end part of the analysis. If we look more closely at the titles, we can see that their names are linked with subtitled curation, e.g. “Kruimels in bed” with Breakfast in Bed, Morning Tea, Sunny Side Up, etc. On the fourth level, we see the description of the playlists (Figure 16).
FIGURE 16 - SECTIONS OF FEATURED PLAYLISTS
The playlist titles have been observed since they address the users, explaining the exact feeling for which the playlists have been prepared. For instance, the playlist Breakfast in Bed description says: “Rustic, romantic acoustic to get your day off to a sweet start” (Figure 16). The description specifies which playlist should be played and in some cases it already suggests which genre of music is offered and what the context is. The second level is the playlists’ picture, which works as a playlist packaging. Covers are usually a combination
43 of a photo and designed title text that even more closely specifies the notion of the playlist. The last three levels are creating a closed group linking to one particular playlist, while the first level links to some of the playlists. Where we can identify this connection is where the context-curated recommendation takes effect.
Each time that a user accesses the featured playlists, they are offered 12 different playlists edited for this time. The duration of the playlists is usually longer than one hour and a half. At least, the shortest playlist that was found was an hour and 30 minutes long. I encountered more playlists that were extensively longer, up to five hours and 50 minutes. Spotify editors create all of these playlists. This can be seen by opening each playlist and looking at the creator. In all cases, the user that created the playlists is connected with Spotify (Figure 17).
FIGURE 17 - FEATURED PLAYLISTS EDITORS
As can be seen here, playlists are created by editors, although the context of the playlists is generated algorithmically. This means that Featured Playlists can be seen as a partially algorithmically and partially editorial curated music offered by Spotify. There are several editors or playlist creators within the Featured Playlists. However, all of them, as I stated beforehand, are official Spotify users. I identified users like Spotify, Spotify Netherlands, Spotify UK, Spotify Deutschland, etc.
I have introduced all of the features presented by the Featured Playlists functionalities but this is not the only part of the software where I have encountered the principles of context- curated recommendations.
44 4.1.2. ANALYSIS SPONSORED CONTENT ALGORITHMS As I am accessing the software with a free account, my research account is subjected to commercials. Within the Browse section of Spotify, on the Overview tab, there is a part of the content section designed for commercials. In some cases, pictures of promoted brands occupy this space. Using the terminology provided by Spotify for Brands, they call this space “Homepage Takeover”. In this space, not only images but also interactive media can be inserted. What is of concern in this thesis is when companies promote their playlists. When this happens, a functionality appears within the Homepage Takeover section similar to Featured Playlists. In the same way as Featured Playlist are addressing the users, the branded playlists are addressing the user to satisfy their access momentum with the selection of context-curated recommendations (see Figure 18). These promoted playlists are
FIGURE 18 - SPONSORED PLAYLISTS SUBJECTED TO CONTEXT ALGORITHM designed similarly as the Featured Playlists are. On the top, there is a title that addresses the context: “De ideale playlist voor je zondagochtend. Pick & Play:” (Figure 18). This screenshot was taken on 17 May 2015 at 11:39. This type of promotion is combining two different promotion formats. One is the already described Homepage Takeover, and the second is a Branded Playlist that allows brands to generate playlists with brand logo and custom text (“Branded Playlist”). Again here, the same as in the case of Featured Playlists, we are faced with the editorial selection, with Branded Playlists and with the algorithmic context-curated recommendation.
During my research, I have encountered three different users promoting their playlists within the Homepage Takeover using the context-curated algorithm. They are Digster, Filtr, and Topsify. These three users are not only regular users but with a further investigation, I also found a link to their web pages where they talk about the playlists that they offer, although there is no other service mentioned. Filtr explains their services as:
45 Filtr is the world’s leading third party playlist service with over 16 million playlist followers across music services like Spotify, Deezer and YouTube. […] Our mission is to simplify and improve the way people discover and enjoy great music and to enhance the listening experience, by providing rich and meaningful music recommendations, expertly curated to a global audience. Filtr offers playlists for any occasion or mood. Our ambition is to help you find the best music whether you’re looking for the latest hits, a soundtrack for your dinner party or a playlist to boost your workout performance. (“About - Filtr”)
With a closer look at the Terms and Conditions of Filtr, it is revealed that the page is a part of Sony Music Group and that it is concerned with listing/editing of copyrighted music. The same was observed on the web page of Digster and Topsify. Again, there was no specific explanation about what kind of service they are, except in their Terms and Conditions. I determined that Digster was a part of Universal Music Group, while Topsify was identified as being owned by Warner Music Group.
I did not find a proof of user’s ownership only by clicking on brands Terms and Conditions, but I have encountered it as well within the Spotify. While loading one of the sponsored playlists, before the playlist page was fully loaded, the username showed the owner of the profile just briefly (Figure 19).
FIGURE 19 - USERNAME CHANGE WHILE LOADING PLAYLIST
I have provided a descriptive analysis of Spotify features connected with context accessed from a personal computer, and I have explained in depth how Featured Playlists
46 are functioning and how the user see them. I have also introduced an observation of sponsored part of Spotify that as well employ a context-curated algorithm to forward the content to users. At this point, I am switching my focus to Spotify’s mobile application as they have recently added another context-curated recommender functionality.
4.1.3. INTRODUCING SPOTIFY RUNNING On Wednesday, the 20th of May, Spotify updated their mobile application with another context-curated recommendation system, named “Running” (Katz). This update brings another level to what I call the context-curated algorithm. It is true that the users have to announce the primary context, running, to the software; however, the software does the rest. Spotify Running follows the tempo at which the user is running and, on this basis, it matches the tempo of the songs (Katz). The overlapping songs are recommended for to the listener.
Spotify Running is an exhilarating new way of running to music. Every song matches your tempo, every beat drives you on. […] Spotify Running finds your tempo and plays music to match. (“Run like Never Before.”)
Furthermore, not only is the algorithm matching the runners tempo, but it connects with their listening history and suggests to them the music that matches their library and tempo as well (Katz). Additionally the user can select an editorial selection of the songs, again matched with his tempo (Katz).
[Spotify Running offers] recommendations based on your listening history, multiple-genre playlists and original running compositions written by some of the world’s foremost DJs and composers – all tuned to your tempo and seamlessly transitioned to ensure you’ll never miss a beat. (Katz)
What we can see here is again an example of editorial suggestions overlapping the algorithmic ones. Although the users have to interact with the software by announcing their intentions, we can still see the context-curated algorithm at work. Here the algorithm does not address the context based on time, as in the case of Featured Playlist but based on the information that it gathered from the moment in which the user was. Of course, for this to function, a ubiquitous technology is necessary, but there must also be a predisposition to use the application. Here we can see how metadata that Spotify gathered with the Echo
47 Nest database created an option to link it with the context. The Spotify Running application an exemplary case of how to use metadata in order to address the current mood of the user.
Here I conclude my descriptive analysis of the interface context-curated functionalities. In this subchapter, I have presented the newest developments of the software; however, now I return to the Featured Playlists algorithm to observe it from the back-end.
4.2. BACK-END API DATA ANALYSIS AND CONTEXT-CURATED RECOMMENDER
As described in the methodology chapter, I examined the API data of Spotify’s Featured algorithm by exporting it with the use of research software to the research server and, from there, manually to the Excel document. In this chapter, I will present the data and prove that the algorithm is forwarding only context-relevant playlists. All the data gathered was mined for the purposes of this thesis and can be found in Appendix 2, as well as all of arranged/color-coded data in Appendix 3. With the usage of color-coding, I merged the playlists in groups in order to see what kind of contexts the Spotify Featured Playlists algorithm addresses most frequently.
FIGURE 20 - CHART GRAPH OF PLAYLIST GROUPS WITHIN THE RESEARCH WEEK
In Figure 20, I present the number of unique playlists, merged with the groups that appeared in the research week. As mentioned already in the methodology section, we can see that the biggest amount of playlists falls into the group of genre-specific playlists, in total
48 37 playlists (Figure 20). In the descending order, I identified 27 Chill Relaxation playlists, 16 Good Mood playlists, power-ups and positive beats. I specified 12 playlists as Workout playlists, from Punching Beats to running with a specific number of beats per minute (bpm) and, at the same time, 12 playlists prepared for Party. Spotify also offered 10 playlists to help users to focus and 10 playlists to be played while dining. I identified nine playlists as Culture Specific. They contained music from a specific country and playlists prepared for culture specific days. In our case, the country was the Netherlands (e.g. Oranje Cafe!). Six playlists were also specifically prepared for waking up and falling asleep, and six of them made for traveling. Seven playlists were connected with romantic mood and six made for sleeping. In addition to these contexts, I discovered five playlists with context specificity, and four playlists that I could not add into any of the existing groups. What we can see here is that the most of the playlists are prepared for leisure time. Seven out of 14 playlists (Chill, Good Mood, Party, Workout, Dining, Romance, and Sleep) offer listeners with the music that they can listen to in their free time. Only three of them are addressing the context of work or preparing for it, including Focus, Commute, and Getting Ready. Appendix 3 shows which playlists are included in every specific group by using the pre-prepared colour-coded filter. (Figure 21).
FIGURE 21 - EXCEL COLOR FILTER
In the following chapters, I will present the results of gathered data that suggest that Featured Playlists is the type of algorithm that should be put into the group of context- curated recommendations. In general, I have identified six patterns that represent how the context-curated algorithm of Featured Playlists addresses the most probable times when
49 users should perform particular everyday actions. Examining the color-coded data in total, I saw an outstanding pattern on Monday the 27th of April that I addressed in particular as another example of how the context-curated algorithms function. This day was taken into a further investigation, and it will be explained more closely in the next sub-chapter.
FIGURE 22 - PARTY PLAYLISTS
Like the first example, I will introduce the Party playlists and continue with playlists from the Getting Ready group. Examining the data, I have determined that they appear on Thursday and Friday evenings, in the last two time zones, between 9 AM and 11 AM (see Figure 22). This means that the most probable time for most users to have a party is on those two days; however, it was expected that they would be present on Saturday evening as well. As the second sample, I examined the morning time context, 6 AM and 8 AM. Here I have identified that the Featured Playlists algorithm posted all of the playlists from the Getting Ready group as well as a number of playlists from Traveling group. More specifically, this was happening for the both groups during the weekdays excluding the 27th of April (See Figure 23). Again, here we can see that the algorithm is addressing the context in which it is expected were getting ready for work or to travelling to their destination. It would be less likely that the listeners would choose a Getting Ready or Travel playlists on Saturday or Sunday morning or in the evenings.
50
FIGURE 23 - GETTING READY AND COMMUTE PLAYLISTS
The Chill and Sleep groups’ playlists were posted in the evenings; at 9PM and 11PM; during the weekend morning, Chill playlists were offered while Dining playlists were posted during the week mostly between 5 PM and 7 PM; however, during the weekend (Saturday and Sunday), they appeared almost in every time zone of the day. In the morning, playlists were offered that went with the breakfast, during the day, music for brunch and in the evening these were the most common offered types of music in the timeframe from 5 to 7 AM. Focus or work playlists appear most often on Monday and Friday morning and evening, and Wednesday and Thursday in the mid-afternoon. Here, we can see that the algorithm offers relaxation playlists during the evenings and weekends when people have time to relax, as most probably they do not need to work at that time. As for dining, we can see that these kinds of playlists are posted in the late afternoon, when people are most likely to be back from work and during the weekend when it is expected that they have the most time to spend preparing a meal. As for Focus and Work playlists, one could argue that they are being distributed at the times when one could lack work motivation and needed an external boost.
In the following paragraph, there is some interesting data although it is difficult to interpret. Workout playlists extend throughout the week, excluding the 27th of April. There is an unconventional pattern in publishing the workout playlists. On Monday, Tuesday and Friday, the workout playlists are posted in the afternoon timeframe between 5 and 9 PM, on Wednesday and Thursday they are posted in the morning, at 6 and 8 AM. During the weekend, they are spread throughout the day until 5 AM. Romantic tunes are mostly offered on Sunday and Saturday, with a higher redundancy during the morning and
51 evenings. Some of the romantic playlists can also be seen during the week, most often on Monday, Tuesday and Thursday evening. I have identified five special playlists whose names specify their time of appearance. For example, Weekend Beats are presented during the weekend and Afternoon Acoustic in the afternoon. This group also includes an interesting playlist that combines the day curation and the cultural one. #ThrowbackThursday NL appears within the Featured Playlists on Thursdays and at the same time it contains culture specific content. In addition to the culture specific text, the playlists are still a part of the context specific groups. A few playlists were excluded from the main observations since I was unable to sort them into any of the bigger groups.
FIGURE 24 - OVERVIEW OF THE DATA
In Figure 24, we can see an overview of the data discussed previously. In this graph, all of the values (playlists) are presented that appeared in the dataset and not only unique ones. The total amount of playlists on Monday the 27th and Monday the 4th is 72, of Saturday 2nd 60, while all of the other days are complete datasets of 84 playlists. Moreover, it is necessary to note here that the color-coded data in this chart does not match with one used in Excel. On this graph, it is clearly visible that most of the most commonly presented playlists by Featured Playlists algorithm are genre playlists that are presented approximately 20% of the time. Furthermore, we can see that there is an outstanding amount of Dining and Chill playlists presented during the weekends in comparison to the other weekdays. We can
52 also see that the number of playlists prepared for romance is increasing through the week with a peak on Sunday. We can see that the Culture Specific playlists are equally spread throughout the week while there is an outstanding amount of this group on Monday 27th of April. In general, we can see that the context-curated algorithm, Featured Playlists, presents playlists based on the particular daytime context. In the following chapter, I will address Monday 27th of April to present the fact that the context algorithm not only addresses the most probable narration but also follows cultural trends and habits in order to satisfy users. As we can see in Figure 24, the whole day stands out of conventional proportions, with high values of Party and Culture.
4.2.1. ADVANCED CONTEXT-CURATED RECOMMENDER The case study of the Featured Playlists presented a number of different examples in which contexts are being addressed. Furthermore, with this curated content it is also suggesting what kind of a lifestyle is expected from the user. In summary, what the algorithm of the Featured Playlists is doing is curating the content to satisfy the context in which it predicts the user to be. The data from Monday 27th of April presented an exceptional pattern within the dataset that I will address now.
Defining from which country the user is accessing the Internet is important because of a number of limitations within the software, including legislations, rights, etc. Moreover, it also influences the curation of the content offered to the user. One of the aims of context- curated algorithms is to satisfy the users by presenting local content on a regular basis. In the example of music, the representatives of music industry also claim that:
“…local repertoires remain the lifeblood of the international music industry.” (“Digital Music Report — IFPI — Representing the Recording Industry Worldwide”)
This is only one of the reasons that the context-curated algorithm is suggesting cultural- specific music. What could be understood as an even more representative example of context-curation is addressing a particular day of the year with particular content. For example, on the Christmas evening the context-curated algorithm would suggest Christmas songs. The research software captured one of the culture-specific days. This was Monday 27th of April, a Dutch national holiday or Koningsdag, on which the King’s birthday is celebrated. On this day, we can see that the Featured Playlist algorithm posted only genres and playlists connected with the topic, with playlists prepared specifically for this day
53 including Prinsheerlijk, Oranje Cafe!, Vorstelijke Vreugde, Máximaal, etc. As with any other playlist presented by Featured Playlists, the editors selected the music in order to meet the user expectations for the celebration. On the algorithmic side of this occurrence, we can see that the algorithm not only suggested culture-specific music, but it had the knowledge that this particular day is a celebration and, as a result, it excluded Work playlists and similar and pushed forward the Party ones.
With this, I conclude representation of Spotify’s Featured Playlists algorithm. I have
FIGURE 25 - CONTEXT DATA FROM 27TH OF APRIL presented that as the personalization algorithm is one specific category of recommendation algorithms; context-curated ones are also an important category of recommendation systems that are gaining an increasing role in the software.
54 5. THE PAST IS THE FUTURE
Discussing the future of music recommendations is an ongoing debate; however, based on previously introduced topics, it is possible to identify in which direction the developers of algorithmic recommendation are aiming in the case of music. Observing the algorithmic diagram, presented in the introduction, and combining it with the introduced cases, I have suggested adding the context-curated type of algorithmic recommendation on the third level; on the side of personalized recommendations. Understanding what influences the decisions of the context-curated recommender is essential in recommending improvements for algorithmic suggestions.
As Rob Kitchin stated, algorithms are a part of socio-technical systems (Kitchin 1). In the article Thinking critically about and researching algorithms, he is focusing on the developer interaction with the algorithms and sees them as assemblages that translate logic and practices to the computational language understood by algorithms (Kitchin 7). This is one of the influences that a context-curated recommender has. Furthermore, to address the context-curated recommenders, we need to add another level of socio-technical interaction to describe all of its influences. These influences come into effect only after the algorithms are constructed. Here, we can find an influence that is affecting its inputs. As I will explain in this chapter, is this usually a human entity or an editor, although it can be another algorithm. This is the editor of the content that adapts the inputs of algorithms. In this chapter, I will discuss the relation between human entities and algorithms on both levels, developer and editorial, create a connection with traditional media systems and suggest in which direction the development of recommendations should go.
5.1. THE CURATORS
As introduced previously, when we are looking into the context-curated recommendations we have to consider them as a socio-technical systems that are suggesting music based on the user’s context.
On the developer level, based on the performed research, it is clear that Spotify also participates in editing of the content, and it is not functioning merely as a service provider. By engaging with users and offering them curated content, they affect popular culture. The reason for doing this is to keep the users entertained and to create reasons for the users to use the platform more frequently. Until now, when I was discussing Spotify’s editors, I
55 always referred to them as to external entities, the “super” users that edit the content for the “regular” users and then let algorithms post it on the platform at the right time. The case of Featured Playlists us a representation of that. Algorithms referring to the context provide an overall presentation about how users are interested in different recommendations at different times or contexts (Adomavicius et al. 105). Another example of what I call the context-curated recommendation algorithm is a recommendation system for movies with a contextual recommendation incorporated (Adomavicius et al. 113). The work of Adomavicius et al. is not an isolated example of context-curated recommenders. Park et al. give an example of the recommendation system that could be used on mobile devices, linking user location, time and even weather as information to contextualize the best recommendation for the user (Park, Hong, and Cho 1130). Based on these two examples, we can see that the developers are participating in the curation of content. They define the context and decide on what kind of content should be recommended. Even if developers only define the connections between the context times and metadata tags in the files, they can be considered to be editors.
If we move on to the input level of context-curated algorithms, we can see that input can also be modified by human editors or by algorithms. First let’s have a look at algorithmic ones that employ developers as editors. If we look at the example of Spotify Running, we can see the functionality that fits into the context-curated recommenders. The algorithm takes the contextual data (running tempo) and matches it with the songs’ bpm. The group of music that they link however is not always a selection of songs by human editors, but it can also be linked to the personalization algorithm as well. Now, if we look back to the Featured Playlists algorithm, there is a different kind of modification happening at the input. Human editors or music experts are the ones that select music for playlists that are later filtered by the algorithm to fit the context.
In general, we can see that the curation of music on Spotify is done through the combination of both algorithmic and editorial approaches. Of course, with the new technological developments and the evolution of contexts, we can expect that recommendations will go further toward the algorithmic ones. Because music has a significant influence on its listeners, I encourage recommendations created by a music expert on the input level, rather than a developer by coding the algorithm.
With the development of new context-curated functionalities, Spotify is becoming more similar to traditional broadcast media, specifically traditional FM radio. Spotify’s
56 Featured Playlists algorithm could be in some ways seen as an assemblage of such a broadcast medium. If we consider each playlist as an interactive stream that allows users to navigate through the content, Featured Playlists can be seen as a collection of channels. Furthermore, as playlist editors are selecting music based on the topic of the playlists, we can see a similarity to the role of the radio DJ (Berland 188).
5.2. MEDIA SOFTWARE AND TRADITIONAL BROADCAST STREAM
As I stated previously, we can see that Spotify needs to participate in the music curation in order to keep the users on the platform. Of course, this is not an obligation but it is a tactic to keep the users interested in the platform. For these purposes, Spotify has the Browse section that offers a number of different channels for its users. Not only recommendations are offered as a part of the curated content, but also other information such as charts and information about new music releases keep the platform interesting (Morris). These observations have also been made by the music industry. As Hans-Holger Albrech, Deezer’s chief executive officer stated in IFPI report 2015;
When people move beyond search to experience the full benefit of tailored curation, they are hooked to the service. (“Digital Music Report — IFPI — Representing the Recording Industry Worldwide 2015”)
This reminds us of traditional broadcasting services that provided interesting content to satisfy their followers (Gross, Gross, and Perebinossoff 243). Looking back to Spotify’s editors, developers or music experts, they are arranging the content, and the algorithms are distributing it. This is why one could argue that the media software is, with its further development, looking back to find an example in the broadcast media.
Furthermore, as the software is becoming increasingly ubiquitous, we can see another emulation of the traditional radio station. The potential that music has to satisfy the listener in every moment is the property of ubiquitous listening and, as it used to empower the traditional streams, it now empowers the modern context-curated recommenders, such as Featured Playlists. The combination of software ubiquity and context-curated recommendations is giving even more power to software as a medium. Moreover, we can see that some of the countries are already turning off their analogue traditional broadcasting receivers aiming for digitalized processes only (Ministry of Culture). This can be perceived as digitalization becoming an unavoidable fact; one could argue that software is going to be able to overcome the broadcast media more easily in this manner. Although the
57 digitalization of traditional media is not the same as streaming services, one could argue that in the near future these services will be incorporated in the digital streams as well.
Even if streaming services are not incorporated in these streams, we already see how this software is becoming incorporated in environments once occupied only by traditional broadcast streams. As an example, we can see that Spotify is already available to stream in cars. Some of the car brands embedded Spotify as a luxury music player on a side of traditional radio (“Can I Use Spotify in My Car?”). If we examine the example of Spotify’s availability in the car from the context-curated perspective, we can see that this context was also addressed. Not only that Spotify offers playlists for the road (Figure 25), but also marketing campaigns have already been engaged (“BMW: Great American Road Trips Playlist Generator”).
FIGURE 26 - ROAD TRIP PLAYLISTS
Previously reserved environments, exclusive to radio streams and physical music mediums, are becoming new environments for the streaming services. This gives us another example of media software becoming more similar to broadcast media. This brings a bigger responsibility to streaming services and software media: it is not only about providing access to the users, but also about how to provide a professional curation of the content for every possible context.
As we can see, streaming services have become more similar to traditional media, although they provide many more possibilities than the traditional media did. If we focus only on the curation of the content, with traditional media it was exclusive only for employed editors, while on streaming services every user is allowed to do it. If the users decide to do so, they can offer their playlists to be shared with other users. Although an
58 algorithm does not push their playlists forward, they still participate in curation as they offer their selection to the other users. This puts them in the role of “prosumers”, which was brought by the digital technologies and blurred the barriers between production and consumption (Ritzer 62). The topic of prosumerism represents an important issue within music-streaming platforms; however, due to the limitations of this thesis it cannot be discussed on a sufficient level. Nevertheless, I suggest researching this topic with the case study of music streaming services through the concept of dispositive introduced by Zajc (39). The presented user engagements can be seen as an example that overcomes uniform traditional broadcasts, by empowering them with the editorial possibilities.
What we can see in this chapter is that music-streaming platforms are becoming more engaged in the context where we conventionally encountered broadcast media as a result, music streaming services have adapted to these environments by emulating the traditional streams. Moreover, we can see that they are bringing the curation of music closer to their users, with the usage of personalized algorithms and possibilities to engage with the curation of the platform’s content.
5.3. MOVING BEYOND BROADCAST MEDIA
If we focus back on the curation of the content, created by algorithms and platform editors, we have to think about avoiding subjectivity created by them. According to Beer:
[…]in a broadcast society, there were these gatekeepers, the editors, and they controlled the flows of information. And along came the Internet and it swept them out of the way, and it allowed all of us to connect together, and it was awesome. But that’s not actually what’s happening right now. What we’re seeing is more of a passing of the torch from human gatekeepers to algorithmic ones (Pariser, "Beware Online 'Filter Bubbles.'").
In order to move beyond the created subjectivity that can be applied by editors or algorithms, I am suggest giving the power of decision about what kind of suggestions one should get to the user itself. By embracing users to contribute to the curation of content and by allowing them the creation of playlists, they should move beyond that, which could be achieved by providing the users with an option to engage with the recommendation preferences. There, the users could decide on what kind of suggestions they want to see, choosing whether their listening history should be tracked, deciding if they want to follow the featured content, which editors they want to follow, etc. This would be an extensive dashboard that would allow the users to manipulate the algorithms that they are subjected
59 to. A utopian concept would be to let users view and edit the code of algorithms of the platform. However, opening the code might not be effective, since not every user could be able to edit it, and it is assumed that platforms want to keep the code as a trade secret. These possibilities of users engaging with algorithmic preferences would to some extent provide what Gillespie calls the idea of “algorithmic objectivity” (Gillespie). Not only that, but it would also contain “the power from within”, or “the post-hegemonic power” addressed by Lash and provide some transparency to the algorithmic filtering (Lash 74; Savers 7). Of course, one could argue that the majority of the people would not care to engage with this self-edited filtering. However, I believe that this is not the case for the users who would like to use the platform but not be subjected to the influence of an entity in power. With this, I conclude this last chapter of my thesis and revise it in short in the conclusion that follows.
60 6. CONCLUSION
This research used the recommendation algorithms of music-streaming platform Spotify as a case study to identify a new subgroup within algorithms. As I presented in the introduction chapter, software algorithms are divided into groups. Based on the proofs provided by this research, I suggested creating a subgroup of context-curated recommendation algorithms and opened a debate considering their influence (Figure 1).
As a first step of this thesis, I presented the theoretical framework in which I was working. There, I presented how algorithms are grouped by their function. In the second chapter, I introduced two groups: already known and widely discussed personalization algorithms and newly established context-curated algorithms.
In order to provide proofs of this new subgroup of context-curated algorithms to support my thesis, I took an example of this kind of algorithm from the music streaming service Spotify, specifically their Featured Playlists functionality. To gather data from the platform, I used two different approaches. One was the interface analysis and the second was API data mining. To perform the interface analysis, I took the research on the Stanfill Discursive interface analysis as a basis that I have adapted for platform observations, and for the API analysis I used auditing approach developed by Sandvig et al. (Sandvig et al. 8; Stanfill 4). For the purposes of this thesis, research software was developed that enabled me to gather the necessary algorithmic output data.
This investigation met the requirements to identify the Featured Playlists algorithm as a context-curated recommenders. The Featured Playlists algorithm links user daytime context with an available database of playlists, which address a particular moment of the day, and forwards them to the interface. By observing the interface, I was already able to recognize some patterns of algorithmic behaviour that overlapped with the idea of the context-curated algorithm, although the full conclusion was established by observing the data coming from the API. Observing the provided data, I recognized groups of contexts that the playlists addressed; furthermore, I was able to connect them with the time of day and activity in which the users are most likely to be. The Featured Playlists algorithm, as such, provided the basis to identify a context-curated algorithm, and its specifications can be seen as requirements to mark one as a context-curated algorithm. As for the curation, I have been able to identify that editors of the content prepared the inputs of the algorithm. In the case of Spotify. I have identified two different types of context-curated algorithms: Featured
61 Playlists, which I used as a primary example and the newly developed functionality Spotify Running. Based on this example, I answer the secondary research question of this thesis: what is the relation between algorithmic and editorial suggestions within the recommendation algorithms? In the case of Featured Playlists, the inputs are created by music experts or employed editors, while in the case of Spotify Running the application gains inputs by employing another algorithm. In the latter case, it is the personalization algorithm. Regardless of the fact that the curation algorithm is employing another algorithm to provide input to it, I also asserted that the creators of algorithms are developers and, as their products are regulating what is going to be shown to the users, the developers also have to be considered to editors. Not only those that create inputs, of course, but also those responsible for the creation of the context-curated algorithms.
In summation, with the research performed, I was able to identify another type of algorithms that are subjected to the user context. These algorithms are functioning in two steps. The first is at the input of the algorithm; here a selection of information is created. An editor or another algorithm can do this. In the second step, the context-curated algorithm comes into the effect as it links meta-data information with the user context. That makes a context-curated algorithm the curator of day-to-day expectations.
This kind of curation satisfies a general need in daily life, one that well recognized in traditional broadcast media. Consequently, I made a comparison of the context-curated algorithm with the traditional broadcast radio and deduced that to some extent, these kinds of algorithms could be seen as a modern supplement to broadcast media. This is not only due to the daily curation that appears in both of them but also because of other shared features, such as ubiquity. This part of research has been done only by looking at the overlaps of the context-curated algorithm and FM radio; as a result, further exploration of this connection is suggested as algorithms are to some extent emulating them.
In the theoretical part of this thesis, I presented the fact that the recommendation algorithms are influencing their users by encouraging them to consume what they propose. As the context-curated algorithms are also a part of recommendation algorithms, they are as well the ones affecting the users. What we can see here is that context-curated algorithms are as well a part of algorithmic culture and possess the power to shape users’ taste. Another suggested study of context-curated algorithms would be to observe if algorithms could as be as influential as broadcast media once were.
62 In order to go beyond this power and to create more geniune algorithms, I have proposed an idea that combines the thoughts of transparency by Saver and algorithmic objectivity by Gillespie (Gillespie; Savers 7). My proposal is less radical than some of the already proposed thoughts, where one suggested providing insight to every part of the algorithmic code. My suggestion here is to create a preferences board for every algorithm in use, especially if it is a recommendation algorithm. The suggested preferences board would let users decide how their data should be filtered, edit their privacy settings and, moreover, they would obtain insight in how the algorithm is functioning.
In general, what we can see from this thesis is that algorithms are shaping users’ tastes with the usage of different means of information forwarding. Moreover, the music industry, via music streaming platforms, have also started to rely on algorithms. Not only are music promotional campaigns being done with the usage of algorithms, as presented in the study, but also due to the transformation that is currently underway from traditional broadcast media to streaming and algorithmic emulation of broadcast mediums.
With this thesis, I am opening a debate about the new group of algorithmic suggestions; context-curated algorithms. If we look at this thesis as basis for this type of recommendation systems, I endorse further exploration of this field, by finding new algorithmic examples, looking into their behaviour and exploring the connections with broadcast media. I suggest here that if one explored how broadcast media curated its content in detail, transferred it to the algorithm and enhanced it with digital interactivity, the result would be one of the most user stimulating recommendation algorithms.
63 7. BIBLIOGRAPHY
“About - Filtr.” Filtr. Sony Music. 24 May 2015. < https://www.filtr.com/nl/spotify/about >. “About The Music Genome Project®.” Pandora. Pandora. 20 April 2015. < http://www.pandora.com/about/mgp >. Adomavicius, Gediminas et al. “Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach.” ACM Transactions on Information Systems (TOIS). 23. 1(2005): 103–145. Ahvenniemi, Hannele et al. “2.3 Creative Industries and Bit Bang–how Value Is Created in the Digital Age.” Research Gate. 2014. Research Gate. 5 June 2015. < http://www.researchgate.net/profile/Aqdas_Malik2/publication/260277035_Creati ve_industries_and_bit_bang__how_value_is_created_in_the_digital_age/links/0f31 75307463066f95000000.pdf >. “Algorithmic Culture. ‘Culture Now Has Two Audiences: People and Machines’ — Futurists’ Views.” Medium. 2014. 30 March 2015. < https://medium.com/futurists- views/algorithmic-culture-culture-now-has-two-audiences-people-and-machines- 2bdaa404f643 >. Beer, David. Popular Culture and New Media: The Politics of Circulation. Basingstoke: Palgrave Macmillan, 2013. ---. “Power through the Algorithm? Participatory Web Cultures and the Technological Unconscious.” New Media & Society. 11. 6 (2009): 985–1002. Bernhardsson, Erik. “Music Recommendations @ MLConf 2014.” SlideShare. 2014. 20 April 2015. < http://www.slideshare.net/erikbern/music-recommendations-mlconf-2014 >. ---. “Collaborative Filtering at Spotify.” Slideshare. 2013. 14 June 2015. < http://www.slideshare.net/erikbern/collaborative-filtering-at-spotify-16182818 >. Berland, Jody. “Radio Space and Industrial Time: Music Formats, Local Narratives and Technological Mediation.” Popular Music. 9. 2 (1990): 179–192. “BMW: Great American Road Trips Playlist Generator.” Spotify for Brands. Spotify. 18 June 2015. < https://www.spotify.com/us/brands/gallery/bmw/ >. “Branded Playlist.” Spotify for Brands. Spotify. 24 May 2015. < https://www.spotify.com/us/brands/formats/branded-playlist/ >. “Can I Use Spotify in My Car?” Spotify Support. Spotify. 18 June 2015. < https://support.spotify.com/is/problems/#!/article/can-i-use-spotify-in-my-car >.
64 Celma, Òscar. “Foafing the Music: Bridging the Semantic Gap in Music Recommendation.” The Semantic Web - ISWC 2006. Athens: Springer, 2006. 927–934. Celma, Òscar, and Paul Lamere. “If You like Radiohead, You Might like This Article.” AI Magazine. 32. 3 (2011): 57–66. “Digital Music Report — IFPI — Representing the Recording Industry Worldwide.” 2014. The International Federation of the Phonographic Industry. 27 March 2015. < http://www.ifpi.org/digital-music-report.php > “Digital Music Report — IFPI — Representing the Recording Industry Worldwide 2015.” 2015. The International Federation of the Phonographic Industry. 26 May 2015. < http://www.ifpi.org/digital-music-report.php > “Discover - Learn more/Guides - Spotify.” Spotify Support. Spotify. 29 April 2015. < https://support.spotify.com/us/learn-more/guides/#!/article/Discover >. “European Cloud Computing Strategy.” Digital Agenda for Europe. 2015. European Commission. 10 June 2015. < ec.europa.eu/digital-agenda/en/european-cloud- computing-strategy >. Galloway, Alexander R. Gaming Essays on Algorithmic Culture. Minneapolis: University of Minnesota Press, 2006. Gillespie, Tarleton. "The Relevance of Algorithms.” Media technologies: Essays on Communication, Materiality, and Society. Eds. Tarleton Gillespie, Pablo J. Boczkowski and Kirsten A. Foot. Massachusetts: The MIT Press, 2014. 167 - 195. Gross, Lynne, Brian Gross, and Philippe Perebinossoff. Programming for TV, Radio & The Internet: Strategy, Development & Evaluation. 2 edition. Amsterdam ; Boston: Focal Press, 2005. Hallinan, Blake, and Ted Striphas. “Recommended for You: The Netflix Prize and the Production of Algorithmic Culture.” New Media & Society. (2014): 1-21. Hamilton, Kevin et al. “A Path to Understanding the Effects of Algorithm Awareness.” CHI '14 Extended Abstracts on Human Factors in Computing Systems. New York: ACM Press, (2014). 631–642. Haupt, Jon. “Spotify (review).” Notes. 69. 1 (2012): 132–138. “Homepage Takeover.” Spotify for Brands. Spotify. 16 June 2015. < https://www.spotify.com/us/brands/formats/homepage-takeover/ >. Hracs, Brian J. “A Creative Industry in Transition: The Rise of Digitally Driven Independent Music Production.” Growth and Change. 43. 3 (2012): 442–461.
65 “Inside The Spotify – Echo Nest Skunkworks” TechCrunch. 2014. 13 Apr. 2015. < http://techcrunch.com/2014/10/19/the-sonic-mad-scientists/ >. John B. Meisel, and Timothy S. Sullivan. “The Impact of the Internet on the Law and Economics of the Music Industry.” info. 4. 2 (2002): 16–22. Johnson, Chris. “Algorithmic Music Recommendations at Spotify.” Slideshare. 2014. 7 April 2015. < http://www.slideshare.net/MrChrisJohnson/algorithmic-music- recommendations-at-spotify >. Kassabian, Anahid. Ubiquitous Listening: Affect, Attention, and Distributed Subjectivity. Los Angeles; London: University of California Press, 2013. Katz, Candice. “Say Hello to the Most Entertaining Spotify Ever.” Spotify Blog. 2015 Spotify. 16 June 2015. < https://news.spotify.com/us/2015/05/20/say-hello-to-the-most- entertaining-spotify-ever/ >. Kitchin, Rob. “Thinking Critically about and Researching Algorithms.” The Programmable City Working Paper 5. (2014): 1-29. “Koningsdag.” Het Koninklijk Huis. 2011. 25 May 2015. < http://www.koninklijkhuis.nl/encyclopedie/monarchie/koningsdag/ >. Lash, S. “Power after Hegemony: Cultural Studies in Mutation?” Theory, Culture & Society. 24. 3 (2007): 55–78. Lee, Kibeom, Woon Seung Yeo, and Kyogu Lee. “Music Recommendation in the Personal Long Tail.” WOMRAD-2010 Music Recommendation and Discovery 2010. (2010) Leyshon, Andrew et al. “On the Reproduction of the Musical Economy after the Internet.” Media, Culture & Society. 27. 2 (2005): 177–209. Lomborg, Stine, and Anja Bechmann. “Using APIs for Data Collection on Social Media.” The Information Society. 30. 4 (2014): 256–265. Mackenzie, Adrian. Cutting Code: Software and Sociality. New York: Peter Lang International Academic Publishers, 2006. Makki, Nadia. “The Music Industry of Web 2.0: Embracing the Cloud.” 2012. < http://www.kentlaw.edu/perritt/courses/seminar/nadia%20makki%20Spotify2Final. pdf >. Manovich, Lev. “Media after Software.” Journal of Visual Culture. 12. 1 (2013): 30–37. ---. Software Takes Command. New York; London: Bloomsbury Publishing, 2013. Ministry of Culture. “Radio Digitisation in 2017.” regjeringen.no. 2015. Government of Norway. 27 May 2015. < https://www.regjeringen.no/en/aktuelt/radio-digitisation- in-2017/id2406145/ >.
66 Morozov, Evgeny. The Net Delusion: The Dark Side of Internet Freedom. Reprint edition. New York: PublicAffairs, 2012. “New on Spotify.” Spotify. 2015. 23 May 2015. < https://www.spotify.com/us/now/ >. Niemelä, Eila, and Juhani Latvakoski. “Survey of Requirements and Solutions for Ubiquitous Software.” Proceedings of the 3rd International Conference on Mobile and Ubiquitous Multimedia. (2004); 71-78. Pariser, Eli. The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think. London: Penguin, 2011. ---. "Beware Online 'Filter Bubbles.'" TED. March 2014. 1 May 2015. < http://www.ted.com/talks/eli_pariser_beware_online_filter_bubbles >. Park, Moon-Hee, Jin-Hyuk Hong, and Sung-Bae Cho. “Location-Based Recommendation System Using Bayesian User’s Preference Model in Mobile Devices.” Ubiquitous Intelligence and Computing. (2007): 1130–1139. Ritzer, George. “Focusing on the Prosumer.” Prosumer Revisited. Ed. Birgit Blättel-Mink and Kai-Uwe Hellmann. VS Verlag für Sozialwissenschaften, 2010. 61–79. “Run like Never Before.” Spotify. 2015. 23 May 2015. < https://www.spotify.com/us/running/ >. Sandvig, Christian et al. “Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms.” Seattle: Data and Discrimination: Converting Critical Concerns into Productive Inquiry, 2014. Savers, Nick. “Knowing Algorithms.” Media in Transition 8. (2013): 1-12. “Spotify Developer.” Spotify Developers. 11 June 2015. < https://developer.spotify.com/ >. “Spotify Explained.” Spotify for Artists. 14 Apr. 2015. < http://www.spotifyartists.com/spotify-explained/ >. Stanfill, M. “The Interface as Discourse: The Production of Norms through Web Design.” New Media & Society. (2014): 1-16. Striphas, Ted. “What Is an Algorithm?” Culture Digitally. 2012. 12 June 2015. < http://culturedigitally.org/2012/02/what-is-an-algorithm/ >. Team, The Spotify. “20 Million Reasons to Say Thanks.” Spotify Blog. 2015. Spotify. 11 June 2015. < https://news.spotify.com/no/2015/06/10/20-million-reasons-to-say-thanks/ >. “The Echo Nest Joins Spotify!” The Echo Nest Blog. 2014. The Echo Nest. 13 Apr. 2015. < http://blog.echonest.com/post/78749300941/the-echo-nest-joins-spotify >.
67 Zajc, M. “Social Media, Prosumption, and Dispositives: New Mechanisms of the Construction of Subjectivity.” Journal of Consumer Culture. 15. 1 (2015): 28–47.
68 8. APPENDIXES All of the appendixes of this thesis are available online. Please find links to the individual appendix in the following chapters.
8.1. APPENDIX 1 - RESEARCH SOFTWARE CODE https://www.dropbox.com/sh/qbz9vw466d7cnxs/AADtUdvmOHySFDdnhWoaUHVoa?dl=0
Written by: Blaž Blokar
Email: [email protected]
8.2. APPENDIX 2 - RESEARCH SOFTWARE FULL DATASET https://www.dropbox.com/sh/r6xhwtfwst7art4/AADT8gElRtKvFc4FaqAiWzXIa?dl=0
8.3. APPENDIX 3 - EXCEL COLOR CODED DATA https://www.dropbox.com/s/klpitx8k9uyid3i/MAThesis_Research_ColorCoded_Data.xlsx?dl =0 Best viewed in PC version of Microsoft Excel.
69