DOCSLIB.ORG

Item-Based Collaborative Filtering with BERT

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Item-Based Collaborative Filtering with BERT Item-based Collaborative Filtering with BERT Yuyangzi Fu Tian Wang eBay Inc. eBay Inc. [email protected] [email protected] Abstract items and seed items that the user has provided feedback for (which may be implicit e.g. clicking, In e-commerce, recommender systems have become an indispensable part of helping users or explicit e.g. rating), and then select the most sim- explore the available inventory. In this work, ilar items to recommend. Although less impacted we present a novel approach for item-based by data sparsity, due to their reliance on content collaborative filtering, by leveraging BERT to rather than behavior, they can struggle to provide understand items, and score relevancy between novel recommendations which may activate the different items. Our proposed method could user’s latent interests, a highly desirable quality for address problems that plague traditional rec- recommender systems (Castells et al., 2011). ommender systems such as cold start, and Due to the recent success of neural networks in ”more of the same” recommended content. We conducted experiments on a large-scale real- multiple AI domains (LeCun et al., 2015) and their world dataset with full cold-start scenario, and superior modeling capacity, a number of research the proposed approach significantly outper- efforts have explored new recommendation algo- forms the popular Bi-LSTM model. rithms based on Deep Learning (see, e.g., Barkan and Koenigstein, 2016; He et al., 2017; Hidasi et al., 1 Introduction 2015; Covington et al., 2016). Recommender systems are an integral part of e- In this paper, we propose a novel approach for commerce platforms, helping users pick out items item-based collaborative filtering, by leveraging the of interest from large inventories at scale. Tra- BERT model (Devlin et al., 2018) to understand ditional recommendation algorithms can be di- item titles and model relevance between different vided into two types: collaborative filtering-based items. We adapt the masked language modelling (Schafer et al., 2007; Linden et al., 2003) and and next sentence prediction tasks from the nat- content-based (Lops et al., 2011; Pazzani and ural language context to the e-commerce context. Billsus, 2007). However, these have their own The contributions of this work are summarized as limitations when applied directly to real-world e- follows: commerce platforms. For example, traditional user- based collaborative filtering recommendation al- • Instead of relying on unique item identifier gorithms (see, e.g., Schafer et al., 2007) find the to aggregate history information, we only most similar users based on the seed user’s rated use item’s title as content, along with token items, and then recommend new items which other embeddings to solve the cold start problem, users rated highly. For item-based collaborative which is the main shortcoming of traditional filtering (see, e.g., Linden et al., 2003), given a recommendation algorithms. seed item, recommended items are chosen to have • By training model with user behavior data, most similar user feedback. However, for highly our model learns user’s latent interests more active e-commerce platforms with large and con- than item similarities, while traditional rec- stantly changing inventory, both approaches are ommendation algorithms and some pair-wise severely impacted by data sparsity in the user-item deep learning algorithms only provide similar interaction matrix. items which users may have bought. Content-based recommendation algorithms cal- culate similarities in content between candidate • We conduct experiments on a large-scale e- 54 Proceedings of the 3rd Workshop on e-Commerce and NLP (ECNLP 3), pages 54–58 Online, July 10, 2020. c 2020 Association for Computational Linguistics commerce dataset, demonstrating the effec- 2.1.1 Next Purchase Prediction tiveness of our approach and producing rec- The goal of this task is to predict the next item ommendation results with higher quality. a user is going to purchase given the seed item he/she has just bought. We start with a pre-trained 2 Item-based Collaborative Filtering BERTbase model, and fine-tune it for our next pur- with BERT chase prediction task. We feed seed item as sen- As mentioned earlier, for a dynamic e-commerce tence A, and target item as sentence B. Both item platform, items enter and leave the market contin- titles are concatenated and truncated to have at uously, resulting in an extremely sparse user-item most 128 tokens, including one [CLS] and two interaction matrix. In addition to the challenge of [SEP] tokens. For a seed item, its positive items long-tail recommendations, this also requires the are generated by collecting items purchased within recommender system to be continuously retrained the same user session, and the negative ones are I and redeployed in order to accommodate newly randomly sampled. Given the positive item set p, I listed items. To address these issues, in our pro- and the negative item set n, the cross-entropy loss posed approach, instead of representing each item for next purchase prediction may be computed as with a unique identifier, we choose to represent L = − X log p(i ) − X log(1 − p(i )): each item with its title tokens, which are further np j j ij 2Ip ij 2In mapped to a continuous vector representation. By (1) doing so, essentially two items with the same title would be treated as the same, and can aggregate 2.1.2 Masked Language Model user behaviors accordingly. For a newly listed item As the distribution of item title tokens is differ- in the cold-start setting, the model can utilize the ent from the natural language corpus used to train similarity of the item title to ones observed before BERTbase, we further fine-tune the model for the to find relevant recommended items. masked language model (MLM) task as well. In The goal of item-based collaborative filtering is the masked language model task, we follow the to score the relevance between two items, and for training schema outlined in Devlin et al.(2018) a seed item, the top scored items would be recom- wherein 15% of the tokens in the title are chosen mended as a result. Our model is based on BERT to be replaced by [MASK], random token, or left (Devlin et al., 2018). Rather than the traditional unchanged, with a probability of 80%, 10% and RNN / CNN structure, BERT adopts transformer 10% respectively. Given the set of chosen tokens encoder as a language model, and use attention M, the corresponding loss for masked language mechanism to calculate the relationship between model is input and output. The training of BERT model can X be divided into two parts: Masked Language Model Llm = − log p(mi): (2) and Next Sentence Prediction. We re-purpose these mi2M Masked Lan- tasks for the e-commerce context into The whole model is optimized against the joint loss guage Model on Item Titles, and Next Purchase Llm + Lnp. Prediction. Since the distribution of item title to- kens differs drastically from words in natural lan- 2.1.3 Bi-LSTM Model (baseline) guage which the original BERT model is trained As the evaluation is conducted on the dataset hav- on, retraining the masked language model allows ing a complete cold-start setting, for the sake of better understanding of item information for our comparison, we build a baseline model consisting use-case. Next Purchase Prediction can directly be of a title token embedding layer with 768 dimen- used as the relevance scoring function for our item sions, a bidirectional LSTM layer with 64 hidden collaborative filtering task. units, and a 2-layer MLP with 128 and 32 hidden units respectively. For every pair of items, the two 2.1 Model titles are concatenated into a sequence. After go- Our model is based on the architecture of BERTbase ing through the embedding layer, the bidirectional (Devlin et al., 2018). The encoder of BERTbase LSTM reads through the entire sequence and gener- contains 12 Transformer layers, with 768 hidden ates a representation at the last timestep. The MLP units, and 12 self-attention heads. layer with logistic function produces the estimated 55 Prec Prec Recall NDCG Method We observe that the proposed BERT model @1 @10 @10 @10 greatly outperforms the LSTM-based model. When Bi-LSTM 0.064 0.029 0.295 0.163 only fine-tuned for the Next Purchase Prediction BERTbase w/o MLM 0.263 0.057 0.572 0.408 BERTbase 0.555 0.079 0.791 0.669 task, our model exceeds the baseline by 310.9%, 96.6%, 93.9%, and 150.3% in precision@1, preci- Table 1: Result on ranking the item sion@10, recall@10, and NDCG@10 respectively. When fine tuning for the masked language model probability score. The baseline model is trained task is added, we see the metrics improved further using the same cross-entropy loss shown in Eq.1. by another 111.0%, 38.6%, 38.3%, and 64.0%. From the experiment, the superiority of proposed 2.2 Dataset BERT model for item-based collaborative filter- We train our models on an e-commerce website ing is clear. It is also clear that adapting the to- data. We collected 8,001,577 pairs of items, of ken distribution for the e-commerce context with which 33% are co-purchased (BIN event) within masked language model within BERT is essential the same user session, while the rest are randomly for achieving the best performance. sampled as negative samples. 99.9999% of entries In order to visually examine the quality of rec- of the item-item interaction matrix is empty. The ommendations, we present the recommended items sparsity of data forces the model to focus on gener- for two different seed items in Table.2.
Load more

Recommended publications
  • The Use of Time Dimension in Recommender Systems for Learning
    The Use of Time Dimension in Recommender Systems for Learning Eduardo José de Borba1, Isabela Gasparini1 and Daniel Lichtnow2 1Graduate Program in Applied Computing (PPGCA), Department of Computer Science (DCC), Santa Catarina State University (UDESC), Paulo Malschitzki 200, Joinville, Brazil 2Polytechnic School, Federal University of Santa Maria (UFSM), Av. Roraima 1000, Santa Maria, Brazil Keywords: Recommender System, Context-aware, Time, Learning. Abstract: When the amount of learning objects is huge, especially in the e-learning context, users could suffer cognitive overload. That way, users cannot find useful items and might feel lost in the environment. Recommender systems are tools that suggest items to users that best match their interests and needs. However, traditional recommender systems are not enough for learning, because this domain needs more personalization for each user profile and context. For this purpose, this work investigates Time-Aware Recommender Systems (Context-aware Recommender Systems that uses time dimension) for learning. Based on a set of categories (defined in previous works) of how time is used in Recommender Systems regardless of their domain, scenarios were defined that help illustrate and explain how each category could be applied in learning domain. As a result, a Recommender System for learning is proposed. It combines Content-Based and Collaborative Filtering approaches in a Hybrid algorithm that considers time in Pre- Filtering and Post-Filtering phases. 1 INTRODUCTION potential to improve the quality of recommendation (Campos et al., 2014). Nowadays, there are distinct educational approaches, This work aims to identify how Time-aware RS e.g. online learning, blended learning, face-to-face (Context-aware RS that uses time context) can be learning, etc.
    [Show full text]
  • A Grouplens Perspective
    From: AAAI Technical Report WS-98-08. Compilation copyright © 1998, AAAI (www.aaai.org). All rights reserved. RecommenderSystems: A GroupLensPerspective Joseph A. Konstan*t , John Riedl *t, AI Borchers,* and Jonathan L. Herlocker* *GroupLensResearch Project *Net Perceptions,Inc. Dept. of ComputerScience and Engineering 11200 West78th Street University of Minnesota Suite 300 Minneapolis, MN55455 Minneapolis, MN55344 http://www.cs.umn.edu/Research/GroupLens/ http://www.netperceptions.com/ ABSTRACT identifying sets of articles by keyworddoes not scale to a In this paper, wereview the history and research findings of situation in which there are thousands of articles that the GroupLensResearch project I and present the four broad contain any imaginable set of keywords. Taken together, research directions that we feel are most critical for these two weaknesses represented an opportunity for a new recommender systems. type of filtering, that would focus on finding which INTRODUCTION:A History of the GroupLensProject available articles matchhuman notions of quality and taste. The GroupLens Research project began at the Computer Such a system would be able to produce a list of articles Supported Cooperative Work (CSCW)Conference in 1992. that each user wouldlike, independentof their content. Oneof the keynote speakers at the conference lectured on a Wedecided to apply our ideas in the domain of Usenet his vision of an emerging information economy,in which news. Usenet screamsfor better information filtering, with most of the effort in the economywould revolve around hundreds of thousands of articles posted daily. Manyof the production, distribution, and consumptionof information, articles in each Usenet newsgroupare on the sametopic, so rather than physical goods and services.
    [Show full text]
  • Mobile Application Recommender System
    UPTEC IT 10 025 Examensarbete 30 hp December 2010 Mobile Application Recommender System Christoffer Davidsson Abstract Mobile Application Recommender System Christoffer Davidsson Teknisk- naturvetenskaplig fakultet UTH-enheten With the amount of mobile applications available increasing rapidly, users have to put a lot of effort into finding applications of interest. The purpose of this thesis is to Besöksadress: investigate how to aid users in the process of discovering new mobile applications by Ångströmlaboratoriet Lägerhyddsvägen 1 providing them with recommendations. A prototype system is then built as a Hus 4, Plan 0 proof-of-concept. Postadress: The work of the thesis is divided into three phases where the aim of the first phase is Box 536 751 21 Uppsala to study related work and related systems to identify promising concepts and features. During the second phase, a prototype system is designed and implemented. Telefon: The outcome and result of the first two phases is then evaluated and analyzed in the 018 – 471 30 03 third and final phase. Telefax: 018 – 471 30 00 The prototype system integrates and extends an existing recommender engine previously used to recommend media items. As a part of the system, an Android Hemsida: application is developed, which observes user actions and presents recommended http://www.teknat.uu.se/student applications to the user. In parallel to the development, the system was tested by a small group of users recruited among colleagues at Ericsson. The data generated during this test period is analyzed to show the usefulness of observed user actions over explicit ratings and the dependency on context for application usage.
    [Show full text]
  • A Systematic Review and Taxonomy of Explanations in Decision Support and Recommender Systems
    Noname manuscript No. (will be inserted by the editor) A Systematic Review and Taxonomy of Explanations in Decision Support and Recommender Systems Ingrid Nunes · Dietmar Jannach Received: date / Accepted: date Abstract With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems con- stantly arise. In this work, we systematically review the literature on expla- nations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice- giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when de- signing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objec- tive, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated. Keywords Explanation · Decision Support System · Recommender System · Expert System · Knowledge-based System · Systematic Review · Machine Learning · Trust · Artificial Intelligence I.
    [Show full text]
  • User Data Analytics and Recommender System for Discovery Engine
    User Data Analytics and Recommender System for Discovery Engine Yu Wang Master of Science Thesis Stockholm, Sweden 2013 TRITA-ICT-EX-2013: 88 User Data Analytics and Recommender System for Discovery Engine Yu Wang [email protected] Whaam AB, Stockholm, Sweden Royal Institute of Technology, Stockholm, Sweden June 11, 2013 Supervisor: Yi Fu, Whaam AB Examiner: Prof. Johan Montelius, KTH Abstract On social bookmarking website, besides saving, organizing and sharing web pages, users can also discovery new web pages by browsing other’s bookmarks. However, as more and more contents are added, it is hard for users to find interesting or related web pages or other users who share the same interests. In order to make bookmarks discoverable and build a discovery engine, sophisticated user data analytic methods and recommender system are needed. This thesis addresses the topic by designing and implementing a prototype of a recommender system for recommending users, links and linklists. Users and linklists recommendation is calculated by content- based method, which analyzes the tags cosine similarity. Links recommendation is calculated by linklist based collaborative filtering method. The recommender system contains offline and online subsystem. Offline subsystem calculates data statistics and provides recommendation candidates while online subsystem filters the candidates and returns to users. The experiments show that in social bookmark service like Whaam, tag based cosine similarity method improves the mean average precision by 45% compared to traditional collaborative method for user and linklist recommendation. For link recommendation, the linklist based collaborative filtering method increase the mean average precision by 39% compared to the user based collaborative filtering method.
    [Show full text]
  • A Recommender System for Groups of Users
    PolyLens: A Recommender System for Groups of Users Mark O’Connor, Dan Cosley, Joseph A. Konstan, and John Riedl Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455 USA {oconnor;cosley;konstan;riedl}@cs.umn.edu Abstract. We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users. We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations. Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email. Introduction Recommender systems (Resnick & Varian, 1997) help users faced with an overwhelming selection of items by identifying particular items that are likely to match each user’s tastes or preferences (Schafer et al., 1999). The most sophisticated systems learn each user’s tastes and provide personalized recommendations. Though several machine learning and personalization technologies can attempt to learn user preferences, automated collaborative filtering (Resnick et al., 1994; Shardanand & Maes, 1995) has become the preferred real-time technology for personal recommendations, in part because it leverages the experiences of an entire community of users to provide high quality recommendations without detailed models of either content or user tastes.
    [Show full text]
  • Chapter: Music Recommender Systems
    Chapter 13 Music Recommender Systems Markus Schedl, Peter Knees, Brian McFee, Dmitry Bogdanov, and Marius Kaminskas 13.1 Introduction Boosted by the emergence of online music shops and music streaming services, digital music distribution has led to an ubiquitous availability of music. Music listeners, suddenly faced with an unprecedented scale of readily available content, can easily become overwhelmed. Music recommender systems, the topic of this chapter, provide guidance to users navigating large collections. Music items that can be recommended include artists, albums, songs, genres, and radio stations. In this chapter, we illustrate the unique characteristics of the music recommen- dation problem, as compared to other content domains, such as books or movies. To understand the differences, let us first consider the amount of time required for ausertoconsumeasinglemediaitem.Thereisobviouslyalargediscrepancyin consumption time between books (days or weeks), movies (one to a few hours), and asong(typicallyafewminutes).Consequently,thetimeittakesforausertoform opinions for music can be much shorter than in other domains, which contributes to the ephemeral, even disposable, nature of music. Similarly, in music, a single M. Schedl (!)•P.Knees Department of Computational Perception, Johannes Kepler University Linz, Linz, Austria e-mail: [email protected]; [email protected] B. McFee Center for Data Science, New York University, New York, NY, USA e-mail: [email protected] D. Bogdanov Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain e-mail: [email protected] M. Kaminskas Insight Centre for Data Analytics, University College Cork, Cork, Ireland e-mail: [email protected] ©SpringerScience+BusinessMediaNewYork2015 453 F. Ricci et al. (eds.), Recommender Systems Handbook, DOI 10.1007/978-1-4899-7637-6_13 454 M.
    [Show full text]
  • A Multitask Ranking System
    Recommending What Video to Watch Next: A Multitask Ranking System Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed Chi Google, Inc. {zhezhao,lichan,liwei,jilinc,aniruddhnath,shawnandrews,aditeek,nlogn,xinyang,edchi}@google.com ABSTRACT promising items. We present experiments and lessons learned from In this paper, we introduce a large scale multi-objective ranking building such a ranking system on a large-scale industrial video system for recommending what video to watch next on an indus- publishing and sharing platform. trial video sharing platform. The system faces many real-world Designing and developing a real-world large-scale video recom- challenges, including the presence of multiple competing ranking mendation system is full of challenges, including: objectives, as well as implicit selection biases in user feedback. To • There are often diferent and sometimes conficting objec- tackle these challenges, we explored a variety of soft-parameter tives which we want to optimize for. For example, we may sharing techniques such as Multi-gate Mixture-of-Experts so as to want to recommend videos that users rate highly and share efciently optimize for multiple ranking objectives. Additionally, with their friends, in addition to watching. we mitigated the selection biases by adopting a Wide & Deep frame- • There is often implicit bias in the system. For example, a user work. We demonstrated that our proposed techniques can lead to might have clicked and watched a video simply because it substantial improvements on recommendation quality on one of was being ranked high, not because it was the one that the the world’s largest video sharing platforms.
    [Show full text]
  • When Diversity Met Accuracy: a Story of Recommender Systems †
    proceedings Extended Abstract When Diversity Met Accuracy: A Story of Recommender Systems † Alfonso Landin * , Eva Suárez-García and Daniel Valcarce Department of Computer Science, University of A Coruña, 15071 A Coruña, Spain; [email protected] (E.S.-G.); [email protected] (D.V.) * Correspondence: [email protected]; Tel.: +34-881-01-1276 † Presented at the XoveTIC Congress, A Coruña, Spain, 27–28 September 2018. Published: 14 September 2018 Abstract: Diversity and accuracy are frequently considered as two irreconcilable goals in the field of Recommender Systems. In this paper, we study different approaches to recommendation, based on collaborative filtering, which intend to improve both sides of this trade-off. We performed a battery of experiments measuring precision, diversity and novelty on different algorithms. We show that some of these approaches are able to improve the results in all the metrics with respect to classical collaborative filtering algorithms, proving to be both more accurate and more diverse. Moreover, we show how some of these techniques can be tuned easily to favour one side of this trade-off over the other, based on user desires or business objectives, by simply adjusting some of their parameters. Keywords: recommender systems; collaborative filtering; diversity; novelty 1. Introduction Over the years the user experience with different services has shifted from a proactive approach, where the user actively look for content, to one where the user is more passive and content is suggested to her by the service. This has been possible due to the advance in the field of recommender systems (RS), making it possible to make better suggestions to the users, personalized to their preferences.
    [Show full text]
  • Recommender Systems User-Facing Decision Support Systems
    Recommender Systems User-Facing Decision Support Systems Michael Hahsler Intelligent Data Analysis Lab (IDA@SMU) CSE, Lyle School of Engineering Southern Methodist University EMIS 5/7357: Decision Support Systems February 22, 2012 Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 1 / 44 Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 2 / 44 Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 3 / 44 Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 4 / 44 Table of Contents 1 Recommender Systems & DSS 2 Content-based Approach 3 Collaborative Filtering (CF) Memory-based CF Model-based CF 4 Strategies for the Cold Start Problem 5 Open-Source Implementations 6 Example: recommenderlab for R 7 Empirical Evidence Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 5 / 44 Decision Support Systems ? Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 6 / 44 Decision Support Systems \Decision Support Systems are defined broadly [...] as interactive computer-based systems that help people use computer communications, data, documents, knowledge, and models to solve problems and make decisions." Power (2002) Main Components: Knowledge base Model User interface Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 6 / 44 ! A recommender system is a decision support systems which help a seller to choose suitable items to offer given a limited information channel. Recommender Systems Recommender systems apply statistical and knowledge discovery techniques to the problem of making product recommendations. Sarwar et al. (2000) Advantages of recommender systems (Schafer et al., 2001): Improve conversion rate: Help customers find a product she/he wants to buy.
    [Show full text]
  • Evaluating Recommendation Systems
    Evaluating Recommendation Systems Guy Shani and Asela Gunawardana Abstract Recommender systems are now popular both commercially and in the research community, where many approaches have been suggested for providing recommendations. In many cases a system designer that wishes to employ a rec- ommendation system must choose between a set of candidate approaches. A first step towards selecting an appropriate algorithm is to decide which properties of the application to focus upon when making this choice. Indeed, recommendation sys- tems have a variety of properties that may affect user experience, such as accuracy, robustness, scalability, and so forth. In this paper we discuss how to compare recom- menders based on a set of properties that are relevant for the application. We focus on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms. We describe experimental settings appropriate for making choices between algorithms. We review three types of experiments, starting with an offline setting, where recommendation approaches are compared without user interaction, then reviewing user studies, where a small group of subjects experiment with the system and report on the experience, and fi- nally describe large scale online experiments, where real user populations interact with the system. In each of these cases we describe types of questions that can be answered, and suggest protocols for experimentation. We also discuss how to draw trustworthy conclusions from the conducted experiments. We then review a large set of properties, and explain how to evaluate systems given relevant properties. We also survey a large set of evaluation metrics in the context of the property that they evaluate.
    [Show full text]
  • Youtube's Recommendation System and the 2019 Canadian Federal
    UP NEXT: YouTube’s Recommendation System and the 2019 Canadian Federal Election by Daniel Cockcroft A thesis submitted in partial fulfillment of the requirements for the degrees of Master of Arts and Master of Library and Information Studies Digital Humanities and School of Library and Information Studies University of Alberta © Daniel Cockcroft, 2020 ABSTRACT In the months leading up to the 2016 election in the United States, YouTube’s recommendation algorithm decidedly favored pro-Trump videos, fake news and conspiracy theories. In this thesis, I question whether such bias is present in the context of the 2019 federal election in Canada. To do so, I make use of open-source software to gather recommendation data related to three of the candidates: Conservative Party of Canada leader Andrew Scheer, New Democratic Party leader Jagmeet Singh, and Liberal Party of Canada leader Justin Trudeau. Using the same data, I will also study the media bias and factual accuracy of the sources recommended. My results show that YouTube’s recommender system is susceptible to influence by audiences and shows bias towards Andrew Scheer and against Justin Trudeau. Given my results and evidence provided by other researchers, this study stresses the need for ethical algorithm design, including proactive approaches for increased transparency, regulatory oversight, and increased public awareness. ii ACKNOWLEDGEMENTS I would like to extend a big thank you to my supervisory committee, Tami, Astrid, and Harvey for keeping me inspired and asking the right questions. Your countless hours of work have not gone unnoticed, and through your efforts you’ve made me a better researcher and writer.
    [Show full text]