Tag And Tag Based Recommender

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  • 1.Tag & Tag-based Recommenders IBM Research ChinaPresenter: Xiatian Zhang ()Team:

2. About Me 2000-2004, B.S. Math, Central South University 2004-2007, M.S. Computer Science, BUPT 2007-Present, Researcher, Working on Recommender Systems and Data Mining 3. Agenda Social Tagging System and Its Features Tag Recommender Tag-based Recommender 4. Social Tagging A folksonomy is a system of classification derived from the practice and method of collaboratively creating and managing tags to annotate and categorize content; this practice is also known as collaborative tagging, social classification, social indexing, and social tagging. Folksonomy is a portmaneau of folk and taxonomy. Social Tagging boomed from 2004, with the wave of Web 2.0. Delicious Citeulike Bibsonomy Youtube Flickr Dogear A internal social book marking system in IBM 5. Some Insights of Tagging System Shilad Sen et.al., tagging, communities, vocabulary, evolution, CSCW06 Modeling vocabulary evolution Tagging system features Based on Movielens recommender system Personal tendency and community influence Tag displaying strategies and their effects Tag utility 6. Modeling vocabulary evolution 7. Tagging System Features Design Features Tag Sharing Tag Selection Item Ownership Tag Scope Broad Narrow Tag Class Factual Tag Subjective Tag Personal Tag 8. Tagging System in Movielens 9. Personal Tendency How strongly do investment and habit affect personal tagging behavior? 1. Habit and investment influence users tag applications. 2. Habit and investment influence grows stronger as users apply more tags. 3. Habit and investment cannot be the only factors thatcontribute to vocabulary evolution. 10. Community Influence How does the tagging community influence personal vocabulary? 1. Community influence affects a users personal vocabulary. 2. Community influence on a users first tag is stronger for users who have seen more tags. 11. Tag Displaying Strategies Effects 12. Tag Utility 13. Tag Recommender Purpose Encourage users to tag more frequently, apply more tags to anindividual resource, reuse common tags Make user use tags not previously considered. Eliminate Redundant tags Promote a core tag vocabulary steering the user toward adoptingcertain tags while not imposing any strict rules. Avoid ambiguous tags in favor of tags that offer greater informationvalue. 14. Tag Recommender Technologies Naive Methods Most Popular Tags on Resources Most Popular Tags on Users Most Popular Tags on Resources and Users Classical Collaborative Filtering User-KNN Item-KNN Adapted KNN Methods Extend User-Item Matrix Degrade User-Item-Tag Relationship Content-based Method Tensor Method Tensor Factorization Graph Based FolkRank Our Work 15. Adapted KNN Extend UI Matrix 16. Adapted KNN Degrade User-Item-Tag relationship Process TF/IDF on UI, UT, IT P-Core Processing Remove noise data Extract User Model by Hebbian Deflation 17. Tensor Factorization 18. FolkRank PageRankPR( p j ) PR( pi ) (1 d ) / N d p j M ( pi )L( p j ) (1) Personalized PageRankPR( p j ) PR( pi ) (1 d ) pi d p j M ( pi )L( p j )(2) FolkRank1. Compute global PageRank by (1)2. Then for each pair, compute personalized PageRank by (2) p[i] = 1, but p [u] = 1 + |U| and p [r] = 1 + |R|.3. FolkRank = Personalized PageRank - PageRank 19. Our Work Explored and Exploring Methods Non-classical Tensor Fusion Factorization Multi-label Classification by Random Decision Trees, High Speed The performance of both two methods are close to FolkRank Current Progress Shiwan develop a simple graph model Best precision and recall on several datasets compared to other methods We are writing paper targeting ACM RecSys 2010 20. Tag-based Recommender Our Work IUI 2008 Paper, Improved Recommendation based on CollaborativeTagging Behaviors Explored Methods Tensor Factorization Non-classical Tensor and Matrix Fusion Factorization Other Works Shilad Sen, Jesse Vig, and John Riedl, Tagommenders: ConnectingUsers to Items through Tags, WWW 2009 21. IUI 2008 Paper Overview We invent a new collaborative filtering approach TBCF (Tag-based CollaborativeFiltering) based on the semantic distance among tags assigned by different usersto improve the effectiveness of neighbor selection. That is, two users could be considered similar not only if they rated the itemssimilarly, but also if they have similar cognitions over these items. Example Both Bob and Tom may rate the movie Avatar with 5 stars, which indicates they all like this movie very much. Nevertheless, as a 3D fan, Bob appreciates this movie for its high quality 3D animations, while Tom may think that it is a wonderful action movie. 22. Tag-based Collaborative FilteringTag-based User-Item Matrix Item1Item2Item3Item4Alice Art, photo Home, Products Writing, DesignLearning, EducationDanielPhoto, Album,TypewriterTutorial, Training Image Sherry Cleaning Language, StudyMaggiePhotography Ovens Steps 1. Calculate the semantic similarity of tags based on WordNet (for the tags not included in WordNet, calculate the edit-distance instead)2. Calculate the similarity between tag sets3. Calculate the similarity between user u and v by summing up the similarity of tag sets on common pages (tagged by both u & v)4. Find the top-N nearest neighbors of the active user to make the prediction5. Return the top-M predicted items to the active user 23. Tag Similarity Calculation Tag similarity WordNet LSA/PLSA Tag set similarity Hungarian method WordNet Concept TreeWord similarity in WordNetIf x and y are contained in WordNet, dis(x,y) is the shortest path length between x and y. 24. Experimental EvaluationData Set Extract total 8000 users, 5315 pages and 7670 tags from web logs.AlgorithmAverage PrecisionAverage Ranking TBCF 0.27 2.8cosine0.13 1.5Random generated subset Average PrecisionAverage Precision TBCF cosine5000.208 0.1212000 0.182 0.1184000 0.202 0.1736000 0.209 0.180 25. Tagommenders: Connecting Users to Items through Tags 26. Q&A