Toward the Next Generation of Recommender Systems:


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Toward the Next Generation of Recommender Systems:

  1. 1. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions<br />Author: GediminasAdomavicius, and Alexander Tuzhilin<br />Source: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 17, NO. 6, JUNE 2005<br />Vincent Chu 2010/5/24<br />
  2. 2. Syllabus<br />Introduction<br />Recommender Systems classification<br />Extending Recommender Systems<br />Conclusion<br />2<br />
  3. 3. Information are overloaded<br />Thousands of news articles and blog posts each day<br />Millions of movies, books and music tracks online<br />3<br />
  4. 4. Can Google help?<br />Yes, but only when we really know what we are looking for.<br />What if I just want some interesting music tracks?<br /> -btw, what does it mean by ”interesting”?<br />4<br />
  5. 5. What’s recommender system ?<br />It’s everywhere in our real-life<br /> To recommend to us something we may<br /> like<br />How?<br />-Based on our history of using services<br /> -Based on other people like us<br />5<br />
  6. 6.<br />6<br />
  7. 7.<br />7<br />
  8. 8. Classification of RECOMMENDER SYSTEMS<br /><ul><li>Content-based recommendations
  9. 9. Collaborative-Filtering Based recommendations
  10. 10. Hybrid approaches </li></ul>8<br />
  11. 11. Content-Based Methods<br />The content-based approach to recommendation has its roots in information retrieval and information filtering research.<br />The content-based systems are designed mostly to recommend text-based items, the content in these systems is usually described with keywords<br />ex: Documents, Web sites (URLs)<br />9<br />
  12. 12. Content-Based Methods<br />TF-IDF (Term Frequency/Inverse Document Frequency)<br />Content(s) be an item profile<br />Document dj is defined as<br />10<br />
  13. 13. Content-Based Methods<br />ContentBasedProfile(c) be the profile of user c containing tastes and preferences of this user.<br />These profiles are obtained by analyzing the content of the items previously seen and rated by the user and are usually constructed using keyword analysis techniques from information retrieval.<br />11<br />
  14. 14. Content-Based Methods<br />12<br />In content-based systems, u(c,s) defined<br />ContentBasedProfile(c) of user c and Content(s) of document s can be represented as TF-IDF vectors and of keyword weights<br />
  15. 15. Content-Based Methods<br />Limitation<br /><ul><li>Limited Content Analysis
  16. 16. Overspecialization
  17. 17. New User Problem</li></ul>13<br />
  18. 18. Collaborative-Filtering Methods<br />Classification<br />User-based CF<br />Item-based CF<br />14<br />
  19. 19. Collaborative-Filtering Methods<br />15<br />
  20. 20. Collaborative-Filtering Methods<br />Predict a particular user based on the items previouslyrated by other users<br />ex.<br />A, B user are similar(same “peers”)<br />If A like movie ”Hitch”,<br />system will recommend “Hitch” to B.<br />16<br />
  21. 21. Collaborative-Filtering Methods<br />Neighborhood formation-kNN (k nearest neighbors)<br />There are n Users, m Products time complexity of User-based CF=> time complexity of item-based CF=> <br />17<br />
  22. 22. Collaborative-Filtering Methods<br />Memory-based<br />make rating predictions basedon the entire collection of previously rated items by theusers<br />Model-based<br />use the collection of ratings to learn a model<br />18<br />
  23. 23. <ul><li>Memory-based</li></ul> Computed as an aggregate of ratings of other users for same item s:<br /> Advanced<br />19<br />Collaborative-Filtering Methods<br />
  24. 24. <ul><li>Memory-based</li></ul>ex. Correlationex. Cosine-based<br />20<br />Collaborative-Filtering Methods<br />
  25. 25. Collaborative-Filtering Methods<br />Model-basedIn contrast to memory-based methods, model-basedAlgorithms, usethe collection of ratings to learn a model, which is then used to make rating predictions.<br />21<br />
  26. 26. Collaborative-Filtering Methods<br />Model-based-cluster models-Bayesian networks<br />22<br />
  27. 27. Collaborative-Filtering Methods<br />Limitation<br />New User Problem<br />New Item Problem<br />Sparsity<br />23<br />
  28. 28. Hybrid Methods<br />1.Combining Separate Recommenders<br />“Choose the better one alternatively”<br />2.Adding Content-Based Characteristics to Collaborative-Filtering Models<br />“Compare similarity, add profile element “<br />3.Adding Collaborative Characteristics to Content-Based Models<br />24<br />
  29. 29. 25<br />
  30. 30. Extending Capabilities Of Recommender Systems<br />Comprehensive Understanding of Users and Items<br /> the most general rating estimation procedure can be defined as<br />26<br />
  31. 31. Extending Capabilities Of Recommender Systems<br />Multidimensionality of Recommendations<br />Aproblem to do this is how to select certain “what” dimensions and certain “for whom” that do not overlap<br />27<br />
  32. 32. Extending Capabilities Of Recommender Systems<br />Multcriteria Ratings<br />ex.Restaurant:<br /> food, decorate, and service<br />Nonintrusiveness<br />28<br />
  33. 33. Extending Capabilities Of Recommender Systems<br />Flexibility<br />Recommendation Query Language (RQL)<br /> (SQL-like)<br />29<br />
  34. 34. Conclusion<br />Improvement to make recommendation methods more effective and applicable to an even broader range of real-life applications <br />30<br />