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SEMANTICS TECHNOLOGIES IN STREAMING SERVICES By Gonzalo Molina Gallego

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INDEX INTRODUCTION ...... 3 THE ARISING OF STREAMING SERVICES ...... 3 VIDEO STREAMING SERVICES ...... 4 STREAMING SERVICES ...... 5 RECOMMENDER SYSTEMS ...... 5 TYPES OF RECOMMENDER SYSTEMS ...... 5 ...... 5 CONTENT-BASED FILTERING ...... 6 HYBRID RECOMMENDER ...... 6 SEMANTIC ...... 6 PANDORA AND MUSIC GENOME PROJECT ...... 7 SEMANTIC SIMILARITY METHOD ...... 8 MUSIC GENOME PROJECT ...... 8 SUPPORT VECTOR MACHINES ...... 9 MY CONTRIBUTION – TESTING PANDORA...... 9 JINNI AND MOVIE GENOME PROJECT ...... 12 CONCLUSION & CHALLENGES ...... 13 FIGURES ...... 14 REFERENCES ...... 20

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INTRODUCTION

Nowadays we can see the rapid growth of streaming services, they offer us a large collection of media content and it does not need to download whole file. This is possible by the improvement of Internet network, bandwidth and storage. These advances combined with powerful computers are the principal reasons of the popularization of several streaming services. But in these past few years, we also see how the advantages of semantic technologies and the Web 2.0, and now the semantics technologies are present in some streaming services.

On one hand, there are streaming music services, leads by Spotify. Spotify is the most popular streaming music service, it is in almost the whole world and it has a big database of songs, but does not have the best recommender system. Instead, exist other music streaming service, it is not able to use in many countries, but uses semantics technologies for recommend and discover new music that would like to the user. This service is Pandora Internet Radio, based on Music Genome, that offers music streaming and smart recommendations. Pandora is capable to analyses songs and creates a user’s profile based on the tastes of users. All this data about different songs is store in Music Genome and each song has approximately 450 tags or attributes to be compared with user’s tastes.

On the other hand, we have movie streaming services and the most popular is Netflix, but Netflix at the beginning had a problem, it had not a good recommendation system. It created to search the best engine to analyse user’s profile and tastes, but another recommender search arises, this recommender engine is Jinni. Jinni is the Pandora for movies, also uses a database like the Music Genome, but in this case, is Movie Genome, composed by thousands of tags to describe and analyse a movie.

In this paper, we are going to see why semantic recommendation are popular, what is the history of these recommender systems and how semantic recommendation systems work.

THE ARISING OF STREAMING SERVICES

First of all, we should analyse when and how streaming services appears and how impact to people. Big companies like Netflix or Spotify are now increasingly used rather than TV or radio and that’s only the beginning of this new tendency. Streaming media is multimedia that is constantly received by and presented to an end-user while being delivered by a provider. Is an alternative to download because the end-user can use its data file (maybe a movie or music) before the entire file has been transmitted [1]. Through streaming media, the live streaming appears. With the live streaming, we can watch on Internet events happening in real-time, delivered to us via a television signals in real-time.

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With the improvements and the growth of the streaming media and the advances in computer networking, combined with powerful computers and fast Internet, several services or delivers of social media content has been created. The main improvement was the support of RTSP protocol. This protocol is used by media players and streaming services to control the transmission. Figure 1 shows how streaming media works. Nowadays, Jinni is one of the most popular recommendation engine for movies and Pandora for music, they use semantics search to improve the accuracy of the search and recommendation, for that reason, big social media companies are interested in this new technology, but before to talk deeply about these two companies, we should understand the explosive growth of streaming services.

VIDEO STREAMING SERVICES Between the 19th and 20th ages, the television was invented and improved until to be in every house. TV is the main information system and is basically the easier way to influence people, for example the 99% of households in Unites States possess at least one television. According to the A.C. Nielsen Company, the average American spent more than 4 hours TV-watching [2]. Netflix provides streaming media and video on demand (VOD) – VOD are systems which allows users to watch video content when they choose to. Nowadays, Netflix has more than 85 million users in Worldwide and it revenues rise to 6.7$ billion. Can Netflix and new video-streaming systems dethrone Television? Video-streaming systems are used by almost the half of the people in Unites States, more precisely, in the 42% American households. Mainly, the video-streaming systems are more popular between the young people, the older people are still use the pay-TV [3]. As we have said before, each time, more and more teenagers join the new tendency of using other devices to view films instead of TV. In the Figure 2 we see the percentage of time watching TV shows by device in US. The time watching the TV is still the higher one in each range of ages, but if you focus only in the first range and in the last, you can see this tendency of using other devices. Finally, the Figure 3 shows the clear victory of online streaming over rent or purchase movies, in no case people prefer rent or purchase a film, they use online streaming to watch it [4]. The TV is still the favourite device to watch movies or TV shows, but in a few years, we will see how the online streaming platforms grow up and if we combine the advantages of streaming media with the semantic recommendation, streaming platforms will overtake live-TV.

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MUSIC STREAMING SERVICES With Spotify, we can listen our favourite music without download it in our smartphone or computer, because Spotify provides a digital music-streaming service [5]. But not only Spotify is helping music panorama, also there are many big companies like Google Play Music, Amazon Prime Music and Pandora. The global music market has a big problem with the piracy and the last years have been really difficult to music companies, but now tendency is changing to a better situation. The last 2015, streaming music services grow fast – more than 66%– and they overcome music physical format. Streaming music represents the 45% of the global music market revenues, meanwhile only the 39% of the revenues come from physical format sales. It was possible by the advantages of the technologies, the spread of smartphones, high-quality audio and high-quality subscription services. For example, more than 60 million of users have a premium subscription in some music streaming platform [6].

RECOMMENDER SYSTEMS

Pandora and Jinni are capable to recommend and search media content by semantic technologies. Pandora and Music Genome Project were founded at the beginning of 2000s, and Jinni at 2008. Music Genome and Movie Genome store thousands tags to analyse and understand every movie and combining with Jinni and Pandora algorithms, they can recommend a song or a movie to one user based in its tastes. Here we are going to see the beginning of the recommender systems and how the pass of years and advances of technology improved them, mixing these recommender systems with semantic technologies. Recommender systems or recommendation systems are created to filtering items, and will be able to predict different items using ratings or preferences gives by a user [7]. The main goal of recommender systems is generating useful predictions or recommendations of items which might interest to a large collection of users [8]. Nowadays exist several engines or ways to recommend items, but these different engines might differ in the way they analyse data, these three different ways to recommend items are the most used.

TYPES OF RECOMMENDER SYSTEMS COLLABORATIVE FILTERING In the 90s, collaborative filtering (CF) recommender system became really popular. Is an algorithm based in the past ratings or behaviour of others users in the rest of the system. This algorithm assumes the same tastes between several users, this means, it tries to find similarities in rating behaviour among several users, then that

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information is used to recommend items to other users. For example, if a group of users like the same items as other user, this user would like items it does not know yet, but group of others users already know [9]. CF has a big issue, is very limited by others users and it depends on their feedbacks. This issue has the name of cold-start, it appears when content have not rated, so this content will not be recommended to a user. CONTENT-BASED FILTERING The other way to analyse data is content-based filtering (CBF). These algorithms are based on a description of the item and the user’s profile to make useful recommendations. CBF has not the problem of CF system, but it is more complex, it is based on the analysis of attributes or descriptions of items [10]. First, it has to abstract the attributes of the items, after CBF compare these attributes with other item ‘attributes and users’ tastes, then analyse output information, and finally recommend an item to a user. For proper recommendation of items, we need meaningful techniques which could analyse and compare all the data (here is where semantics can help). We can see in the Figure 4 a correct structure or architecture of a CBF and how it works. Pandora and Jinni use this type of recommender system, combined with semantic technologies and the support of Music Genome and Movie Genome. HYBRID RECOMMENDER We can combine several recommender systems to create a hybrid recommender system. This kind of systems combines two or more recommender systems (you can also combine to recommender systems based on the same system) to improve the quality of the recommendation. It has several strategies to combine different recommender systems and depending of which strategy and what types of techniques you use to combine the recommender systems; you will can obtain a useful output or not. Figure 5 shows if you can use a strategy to combine two different recommender systems [11].

SEMANTIC RECOMMENDER SYSTEM A new generation of recommender systems arise in the last few years. The rest of recommender systems have many limitations and the recommendation is not useful, but with the combination of the advantages in semantics technologies and its features such as ontologies or tagging, another recommender systems emerge to make smarter and personalized recommendations. The most important problem and limitation of recommender systems is they cannot understand users and items because they have limited data, that’s mean its predictions are based only in past ratings of other users and keywords. Semantics technologies understand better users and items, therefore the key for an efficient recommender system is better understanding of both, users and items. But what can add semantics technologies to recommender systems to improve the recommendation? Semantics recommender system uses taxonomies based on user/item description to extract weighted ontology topics. It also includes several features of social networking to improve the relations between users, matching users and finding

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similarities between users. It combines semantic items’ similarity, the past feedback or ratings and demographic similarities to match users. Other important feature of semantics, is the tagging. It can create huge databases with tags (Movie Genome and Music Genome) to make easier the analysis of the content. Other approach of tagging is to create folksonomies by collaborative users. Each user has a profile; this profile has many tags (used as items) and the system analyses those tags to understand better the user tastes [12]. The cold-start problem does not appear is this type of system, it is solved with the folksonomies and tagging. But folksonomies and tagging by users have problems too, social tagging has problems, such as the information could be corrupt, sparsity (a lot of tags, maybe with synonyms), polysemy and iosyncrasy (tags for personal information). This problem is important because the quality of the recommendation could not be good. To solve this problem, there are different ways to collect tags. For example, Pandora has not a folksonomy created by users, tags of Music Genome are provided by experts, this tags are objectives and cover multiple aspects. Other way to acquires tags is content-based tagging. To create tags, first it crawls associated information from the Web, converts the data into a suitable representation and generates the tags by algorithms. One interesting tag acquisition method is tagging based on annotations games. It consists in two players watching the same image and writing tags until they write the same tag [13].

PANDORA AND MUSIC GENOME PROJECT

Pandora is a streaming music platform and one of the better recommendation systems [14]. Pandora is combined with Music Genome Project in order to recommend new music to a user. Pandora use CBF with semantic recommendation engine to improve quality of its recommendations and offer personalized music. In the Figure 6 we can see the environment of this platform. We see the servers for the playlists or for the content, and they are connected to the database to obtain songs and the recommendations. We can access to Pandora with several devices like smartphone, tablet or laptop. When Pandora adds semantics to its recommendation engine, enhance the personalization of the items suggested because uses domain ontologies, user’s interests are modelled in a more effective, also applies a model-based inference method to be more accurate and the algorithm responsible for filtering has better performance by use of semantic similarity method [15]. Figure 7 is the steps that Pandora follows to discover new music for us. When we create a new radio station we can rate the song that is playing (like, neutral or not like), depending of your rating, Pandora is learning more about your tastes and it

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recommend you different types of music. Our feedback is stored with the content in the database, in the Figure 8 we can see an example of how all this information is stored. In that picture, we have two different stations of music, one created through the search “Sweet child of mine” and the other with “U2”. Here we have a comparison between the same music in the two different station, each song has several values (spin, feedback, positive, negative…) and the most important, Song Q. The ‘Q’ value indicates the suitability of each song in that station, this value is calculated by this equation Q=((ΣF pos)−(ΣF neg))/(total # of spins). This value is important to understand better the tastes of each user [16]. Other similarity methods have improved collaborative filtering methods too; these systems are called item-based collaborative filtering and they try to find similar items rather than similar users, those items were rated by other users and the future predictions will be generated matching a weighted item with a target weighted user who its ratings are similar than the item. Although this method suggests better than CF, it also has the cold-start problem. We can combine similarity methods with semantic recommendation and it will enhance performance and usefulness of the suggestion, of course the cold-start is not a problem here because we do not need past ratings.

SEMANTIC SIMILARITY METHOD Pandora uses this method to improve quality of recommendations, I will give you an example of how it works. A new composer has a new song (the composer has 0 followers and 0 listeners), this song is jazz, more exactly blues, and this new composer upload the song to Pandora. Pandora includes this song to the Music Genome (it analyses the song and create the tags), then the semantic similarity method compare song attributes and try to find other similar songs (known songs) to recommend that song to users that like the other known songs. What that’s mean? Each component of Music Genome has semantic attributes (tags, structure, relationships with other items and other metainformation) and this information describes the features of the items. Combining semantic information with the user information (feedback and tastes) it will have more useful data and it will give users intelligent recommendations [17]. This method is performed by three algorithms because we will need to calculate the similarities between two items of different users, the user tastes and finally, the prediction of an item. Figure 9 shows 3 algorithms capable to predict item for users using this method. The data needed by them (ratings) are in a weighted graph. These algorithms also includes semantic neighbourhood (compares tastes between users).

MUSIC GENOME PROJECT If we want to understand Pandora, we should know what is the Music Genome Project. Music Genome was created in the 2000, is the most complex and sophistically taxonomy in music terms and it has 10 years of analysed music [18]. Basically, the Music Genome is like a big database, each song in that database have more than 450 music attributes and with this attributes, Pandora can understand the music preferences

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of every user and the characteristics of a song to make good recommendations. Of course, this big database is continuously updated. As we saw before, semantics recommender systems need a taxonomy to suggest different songs to users and the taxonomy of Pandora was created by experts in music. Music Genome Project is not only composed by tags created by the experts, also includes musical structure of each song and each genre. With this ability it analyses deeply each song. SUPPORT VECTOR MACHINES Other important characteristic of Pandora is the ability of extract musical structure of songs, its algorithm that analyses the match between user-item is capable to search attributes of a song for matches of more than 400 parameters. Its search is based on these characteristics of music: musical arrangements, rhythm, musical harmony, lyrics, structure, melody, voice, orchestration, beat, tempo and syncopation. The task of extract and analyse all these data are perform by support vector machines. We can see in the Figure 10 the differences between POP, rock, jazz and classic beats, in the Figure 11 compare LPC-derived cepstrum of different music types – LPC- derived cepstrum is the application of linear predictive analysis to a sample of music, because a singular music sample can be approximated as a linear combination of others – and in the Figure 12 we see the differences between spectral power of POP and classic [19]. These data are store in the Music Genome to be analysed by support vector machines.

MY CONTRIBUTION – TESTING PANDORA I have never heard about Pandora until the last few months, when Google puts on the market its new products, I saw the new Google Home assistant and it can reproduce music from several platforms like Spotify or Pandora. Pandora is less known because it is only available in EEUU and New Zealand, it has 1.000.000 million songs on its database and is more like a radio station. The last week I tested Pandora and I compared my experience with Pandora with Spotify to see the differences. When you enter to Pandora (and you create your user) you have to search an artist or a song to start your own radio station.

In my case, I search the artist from Bergen, Kygo.

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And then, the station is created and the music start with a random song, but one of the most popular songs of this artist.

If you like the song and you want to hear again in your station, you only have to click on the thumb up, if not, thumb down and the song will change automatically. Of course, you do not have to click always one of the thumbs if you have a neutral opinion of that song. I heard several songs, you can see what songs did you hear and if it liked you or not.

One of the most interesting features of this web (it has a lot, like the lyrics of the song, similar artist…) is you can know why Pandora choose that song for you.

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After testing 23 songs, I make this graph:

TESTING PANDORA

4 4 4 4

3 3 3 3 3

2 2 2 2

1 1 1 1 1

0 0 0

-1 -1

-2

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We can see at the beginning of the graph several known songs, from famous artist, then when Pandora has more information about your tastes, it starts to find what genre or style would you like to hear. Pandora recommend better than Spotify, but why? It is so simple, first of all, the cold-start is not a problem because with semantic similarity methods and SVM, Pandora does not need past feedback to recommend new music. On the other hand, Spotify recommend music for you based only in the genre of the song, past feedback (CF) and listeners of that song or artist and your localization. Also, the Spotify’s database is not like Music Genome, Music Genome is more complex and has more attributes per song and user. We compare Pandora with Spotify because they are the more important platforms to listen music, but Spotify was not created to discover new music, Spotify is similar just to a reproducer or music player, Pandora was created to discover new music, personalized music, like an improved radio. In the last years, Spotify works with last.fm, is other platform to recommend music and it uses semantics to find songs, also in the last years Spotify started to work in machine learning to improve its recommender system. I hope Pandora continue growing up because is very useful to find new music and it can help unknown artist to be more famous.

JINNI AND MOVIE GENOME PROJECT

Jinni is a search engine and recommendation engine for short-movies, TV shows and films. It was founded at the beginning of 2008 and Jinni has the capabilities to recommend TV shows, movies, documentary and whatever you can watch on TV or in the cinema. It was based on Music Genome and Pandora. Jinni has provided a real artificial intelligence feeling to the users who watch movies or TV shows on Jinni or other film distribution network using thousands descriptive tags created to analyse, describe and understand all TV shows or movies. Jinni is able to know all the TV content analysing all the descriptions, reviews and metadata to assign between 40 and 60 tags per content title [20]. These thousands descriptive tags form a big genome. This genome receives the name of Movie Genome Project, that approach to indexing movies based on attributes in order to create a personalized movie catalogue [21]. The Jinni’s algorithms also are capable to targeted ads, turning the ads into personal recommendations. By semantic technologies, Jinni can understand the content and match it to the user’s tastes, revolutionizing and creating a next generation of DSP for movies and TV content. Jinni is like Pandora; in the Figure 13 we see how Jinni works. The starting point is Predefined Items List Storage, to avoid the Cold-Start and recommend some items to

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the user. Then, Jinni analyses feedback (and not only feedback, also important information like if the user watch the whole movie or not) and creates a user’s profile to store data about its feedback, information and tastes. Finally, Jinni recommend new content to the user and it waits the feedback and more data to know deeply about the user [22]. Jinni is one of the smartest engines to recommend TV content, but why Jinni is the most popular? This service includes semantic search to interpret queries like the rest of recommendation engines. Jinni, furthermore, can identify concepts within the content. We have different ways to classify the movies, the most common way to do this, is by using keywords or tags; these keywords can be the title of the movie, the name of the director or some actor or actress, a substantive, an adjective or a verb. We assign words to the movies in order to describe the plot of the movies. If we assign many tags to a movie, the recommendation of this movie will be better and more accuracy. For example, we can search House of Cards through the keywords: political, white house and treason. These two websites, http://www.imdb.com and http://www.lazydayapp.com, search for movies by tags. Jinni is also capable to search movies using this technology, the thing that become Jinni is the most popular is the identification of the concept within the content. This means Jinni can recognize some attributes of a movie (plot, mood, tone and structure) automatically. Jinni store all the data about your queries, then analyses these data by Natural Language Processing and Machine Learning methods and it is capable to recommend different kinds of media content depending of your tastes [23].

CONCLUSION & CHALLENGES Streaming content is growing up every day, it can offer us things that live-TV never will can and many of this services are provided by semantic technologies. We have seen how a recommendation system can learn a lot of things about our tastes, our tendencies and our live, in few years this will be in all platforms to give us personalized content and new items. Netflix and Spotify will be more important than TV or whatever, all the people have or will have a powerful device to play this content and the integration of other sensors or systems (smartwatch, Virtual Reality…) probably mean the final of the TV. Semantic technologies in streaming services are now on a starting point, algorithms that are used by Pandora and Jinni will be improve to help us in every moment of our life. The privacy is a risk here but finally we will trust on the legibility of these platforms to let them expand its knowledge about us. I have had problems searching information about the algorithms used by Pandora and Jinni to match user/item, there are a lot of information about recommender systems and how semantics can improve the performance and the personalization of the suggestion, but these data do not go deeply into how it works, only give a theoretical approach

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FIGURES

Figure 1: Here we see the communication between sevaral components is a streaming proccess. The proccess starts with a request of the user to the webpage, then the user can access to metafile content in the server and finally the server creates a RTSP connection with the media player.

Figure 2: In this figure, we can see the percentage of time watching media content depending on each platform/device.

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Figure 3: These percentages are the frequency of streaming, renting and purchasing movies.

Figure 4: Shows a good content-based filtering architecture. We can also see the different steps to recommend items. The first step is the Information Source. It extracts description or attributes of an item and then analyses that description. The analysis is performed by CONTENT ANALIZER, it also extracts n-grams or keywords to produce a structured item representation. It also exists a repository with the feedback of the user, this information is used by the PROFILE LEARNER to create a set of pairs (item representation, rating) and update the information of user’ profile. Finally, the FILTERING COMPONENT predicts whether it is likely to be of interest to the user, comparing user preferences with item representation. The output of this process is a list of recommendations; the user will rate this new list and the process will start again.

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Figure 5: How we can combine different recommender systems and what strategies we can use for combine it.

Figure 6: Environment of Pandora.

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Figure 7: Steps to recommend and discover songs in Pandora.

Figure 8: Storage of each song in several stations.

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Figure 9: Semantic similarity algorithm to predict items to users.

Figure 10: Comparison between different kinds of music by its beats.

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Figure 11: LPC-derived cepstrum of POP, rock and jazz.

Figure 12: Spectral power between POP and classic music.

Figure 13: Jinni environment.

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REFERENCES

1. Streaming Media, https://en.wikipedia.org/wiki/Streaming_media. 2. https://www.csun.edu/science/health/docs/tv&health.html. 3. Todd Spangler, “Streaming Overtakes Live TV Among Consumer Viewing Preferences: Study”. 4. Deloitte, “Digital democracy survey”. 5. Bloomberg, “Company Overview of Spotify AB”. 6. IFPI, “An explosion in global music consumption supported by multiple platforms”. 7. Recommender Systems, https://en.wikipedia.org/wiki/Recommender_system. 8. Prem Melville, “Recommender Systems”. 9. Michael D., John T. & Joseph A., “Collaborative Filtering Recommender Systems”, chapter 2. 10. Pasquale L., Marco de Gemmis & Giovanni S., “Content-based Recommender Systems: State of the Art and Trends”, pages 1-5. 11. Robin Burke, “Hybrid Web Recommender Systems”. 12. Houda O. & Omar N., “Exploiting Semantic Web Technologies for Recommender Systems A Multi View Recommendation Engine”. 13. Leandro B., Alexandros N., Lars S., Robert J., chapter 19, pages 9-12, “Social Tagging Recommender Systems”. 14. Pandora, https://en.wikipedia.org/wiki/Pandora_Radio. 15. Victor C. & Luigi C., “A recommendation system for the semantic web”. 16. Pandora Media Inc, (2001). Methods and systems for utilizing contextual feedback to generate and modify playlists. US7962482B2. 17. Bamshad M. & Xin J., “Using semantic similarity to enhance item-based collaborative filtering”. 18. Pandora about Music Genome Project, http://www.pandora.com/about/mgp. 19. Changsheng X., Namunu C., Xi S, Fang C, Qi T, “Musical genre classification using support vector machines”. 20. Jinni (search engine), https://en.wikipedia.org/wiki/Jinni_(search_engine) 21. Movie Genome, https://en.wikipedia.org/wiki/Movie_Genome 22. JINNI MEDIA LTD, (2016). System Apparatus Circuit Method and Associated Computer Executable Code for Hybrid Content Recommendation. US20140172501A1. 23. Jinni Discovery, http://www.jinni.com/discovery/

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