1. Community-Based Link Prediction/Recommendation in the Bipartite Network of BoardGameGeek.com Brett Boge CS 765 University of Nevada, Reno

2. IntroductionThe ProblemRelated WorksConclusionQuestions / Comments

3. Amazon.comNetflixIMDB

4. Very important for online businesses Drive demand for product Companies have had contests with million dollar prizes to increase performance Recommender Systems

5. IntroductionThe ProblemRelated WorksConclusionQuestions / Comments

6. BoardGameGeek.com 55,000 Board Games 400,000 Users Profile data: Ownership Ratings # of players Price Genre Length

7. Users & Item Profiles Based on content (e.g. genre, demographics, length, etc.) Content Based Users & Items similar to those in the past More abstract, only links matter Collaborative Based

8. 503285? User 1 User 2 Item 1 Item 2

9. IntroductionThe ProblemRelated WorksConclusionQuestions / Comments

10. Memory-based Use entire dataset directly Model-based Create a model based on data Uses model to make recommendations Collaborative Filtering J. S. Breese, et al., "Empirical analysis of predictive algorithms for collaborative filtering," 1998

11. Recommendations are based on the users that have liked items similar to ones the user has liked in the past User-based Collaborative Filtering Recommendations are based on the items rated/bought similarly to other items Item-based Collaborative Filtering

12. kNN based on: Data Normalization Neighbor selection Interpolation weights Improvements to: Data Normalization Interpolation weights The BellKor Algorithm R. M. Bell and Y. Koren, "Improved neighborhood-based collaborative filtering," 2007.

13. Sparsity is an issue Consumer-product matrix looks like: Instead, represent the matrix as a bipartite graph Significantly better results under sparse conditions Computationally expensive Link-analysis approach Z. Huang, et al., "A Link analysis approach to recommendation under sparse data," 2004.

14. Link-analysis approach Z. Huang, et al., "A Link analysis approach to recommendation under sparse data," 2004.

15. Consumer Representativeness Product Representativeness Link-analysis approach Z. Huang, et al., "A Link analysis approach to recommendation under sparse data," 2004. CrPr

16. CF Performs poorly for “cold-start” users Trust-based recommenders work well if a user is at least connected to a large component Sparsity forces a trust-based approach to consider weakly trusted neighbors Added a random walk model to allow for defining and measuring a confidence metric Protects agains things like faked profiles or spammed ratings TrustWalker M. Jamali and M. Ester, "TrustWalker: a random walk model for combining trust-based and item- based recommendation," 2009

17. Require large amount of knowledge about users and items Often use textural information (website recommenders) Explicit or implicit profile generation Can over specialize (some workarounds) Content-Based Filtering G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,"

18. Collaborative: + Cross-genre niches - New users/items - Gray-sheep users Content-based + Handles new items easily Hybrid Filtering R. Burke, "Hybrid recommender systems: Survey and experiments,"

19. Weighted Switched Mixed Feature combination Cascade Methods of Hybrid Filtering R. Burke, "Hybrid recommender systems: Survey and experiments,"

20. Clustering Approach Q. Li and B. M. Kim, "Clustering approach for hybrid recommender system," 2003

21. IntroductionThe ProblemRelated WorksConclusionQuestions / Comments

22. Content vs Collborative Graph-based, use modeified popular algorithms (e,g. PageRank) Similarity metrics important Hybrid models use extra information Recommender Systems

23. IntroductionThe ProblemRelated WorksConclusionQuestions / Comments