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Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
Tag And Tag Based Recommender
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Tag And Tag Based Recommender

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  • 1. Tag & Tag-based Recommenders IBM Research – China Presenter: Xiatian Zhang (张夏天) Team: 赵石顽 张夏天 袁 泉
  • 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, CSCW’06 – 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 user’s 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 user’s personal vocabulary. – 2. Community influence on a user’s 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 an individual resource, reuse common tags – Make user use tags not previously considered. – Eliminate Redundant tags – Promote a core tag vocabulary steering the user toward adopting certain tags while not imposing any strict rules. – Avoid ambiguous tags in favor of tags that offer greater information value.
  • 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  PageRank PR( p j ) PR( pi )  (1 d ) / N  d  p j M ( pi ) L( p j ) (1)  Personalized PageRank PR( p j ) PR( pi )  (1 d ) pi  d  p j M ( pi ) L( p j ) (2)  FolkRank 1. Compute global PageRank by (1) 2. Then for each <user, item> 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 Collaborative Tagging Behaviors – Explored Methods – Tensor Factorization – Non-classical Tensor and Matrix Fusion Factorization  Other Works – Shilad Sen, Jesse Vig, and John Riedl, Tagommenders: Connecting Users to Items through Tags, WWW 2009
  • 21. IUI 2008 Paper Overview  We invent a new collaborative filtering approach TBCF (Tag-based Collaborative Filtering) based on the semantic distance among tags assigned by different users to improve the effectiveness of neighbor selection.  That is, two users could be considered similar not only if they rated the items similarly, 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 Filtering Tag-based User-Item Matrix Item1 Item2 Item3 Item4 Alice Art, photo Home, Products Writing, Design Learning, Education Daniel Photo, Album, Ø Typewriter Tutorial, Training Image Sherry Ø Cleaning Ø Language, Study Maggie Photography Ø Ovens Ø 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 sets 3. 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 prediction 5. 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 Tree Word similarity in WordNet If x and y are contained in WordNet, dis(x,y) is the shortest path length between x and y.
  • 24. Experimental Evaluation Data Set Extract total 8000 users, 5315 pages and 7670 tags from web logs. Algorithm Average Precision Average Ranking TBCF 0.27 2.8 cosine 0.13 1.5 Random generated subset Average Precision Average Precision TBCF cosine 500 0.208 0.121 2000 0.182 0.118 4000 0.202 0.173 6000 0.209 0.180
  • 25. Tagommenders: Connecting Users to Items through Tags
  • 26. Q&A

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