1. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions Author: GediminasAdomavicius, and Alexander Tuzhilin Source: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 17, NO. 6, JUNE 2005 Vincent Chu 2010/5/24

2. Syllabus Introduction Recommender Systems classification Extending Recommender Systems Conclusion 2

3. Information are overloaded Thousands of news articles and blog posts each day Millions of movies, books and music tracks online 3

4. Can Google help? Yes, but only when we really know what we are looking for. What if I just want some interesting music tracks? -btw, what does it mean by ”interesting”? 4

5. What’s recommender system ? It’s everywhere in our real-life To recommend to us something we may like How? -Based on our history of using services -Based on other people like us 5

6. Amazon.com 6

7. Amazon.com 7

9. Collaborative-Filtering Based recommendations

10. Hybrid approaches 8

11. Content-Based Methods The content-based approach to recommendation has its roots in information retrieval and information filtering research. The content-based systems are designed mostly to recommend text-based items, the content in these systems is usually described with keywords ex: Documents, Web sites (URLs) 9

12. Content-Based Methods TF-IDF (Term Frequency/Inverse Document Frequency) Content(s) be an item profile Document dj is defined as 10

13. Content-Based Methods ContentBasedProfile(c) be the profile of user c containing tastes and preferences of this user. 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. 11

14. Content-Based Methods 12 In content-based systems, u(c,s) defined ContentBasedProfile(c) of user c and Content(s) of document s can be represented as TF-IDF vectors and of keyword weights

16. Overspecialization

17. New User Problem13

18. Collaborative-Filtering Methods Classification User-based CF Item-based CF 14

19. Collaborative-Filtering Methods 15

20. Collaborative-Filtering Methods Predict a particular user based on the items previouslyrated by other users ex. A, B user are similar(same “peers”) If A like movie ”Hitch”, system will recommend “Hitch” to B. 16

21. Collaborative-Filtering Methods Neighborhood formation-kNN (k nearest neighbors) There are n Users, m Products time complexity of User-based CF=> time complexity of item-based CF=> 17

22. Collaborative-Filtering Methods Memory-based make rating predictions basedon the entire collection of previously rated items by theusers Model-based use the collection of ratings to learn a model 18

25. Collaborative-Filtering Methods 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. 21

26. Collaborative-Filtering Methods Model-based-cluster models-Bayesian networks 22

27. Collaborative-Filtering Methods Limitation New User Problem New Item Problem Sparsity 23

28. Hybrid Methods 1.Combining Separate Recommenders “Choose the better one alternatively” 2.Adding Content-Based Characteristics to Collaborative-Filtering Models “Compare similarity, add profile element “ 3.Adding Collaborative Characteristics to Content-Based Models 24

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30. Extending Capabilities Of Recommender Systems Comprehensive Understanding of Users and Items the most general rating estimation procedure can be defined as 26

31. Extending Capabilities Of Recommender Systems Multidimensionality of Recommendations Aproblem to do this is how to select certain “what” dimensions and certain “for whom” that do not overlap 27

32. Extending Capabilities Of Recommender Systems Multcriteria Ratings ex.Restaurant: food, decorate, and service Nonintrusiveness 28

33. Extending Capabilities Of Recommender Systems Flexibility Recommendation Query Language (RQL) (SQL-like) 29

34. Conclusion Improvement to make recommendation methods more effective and applicable to an even broader range of real-life applications 30