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Social Recommender Systems

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social relationships provides a new opportunity for improving the quality of recommender systems

Social Recommender Systems

  1. 1. Social Recommender System By: Ibrahim Sana 15.08.08
  2. 2. Agenda <ul><li>Introduction </li></ul><ul><li>Background on Collaborative Filtering </li></ul><ul><li>Collaborative Filtering Limitation </li></ul><ul><li>Using trust in RS </li></ul><ul><li>Related works </li></ul><ul><li>Research methodology </li></ul><ul><li>Evaluation and Results </li></ul><ul><li>Conclusion </li></ul>
  3. 3. Introduction <ul><li>Recommender system (RS) help users find items (e.g., news items, movies) that meet their specific needs. </li></ul><ul><li>Motivation </li></ul><ul><ul><li>Information overload </li></ul></ul><ul><li>Researches in RS focused on developing methods and approaches dealing with the Information overload problem. </li></ul><ul><li>Main Approaches </li></ul><ul><ul><li>Content-Based (Salton, 1989) </li></ul></ul><ul><ul><li>Collaborative filtering/Social Filtering (Goldberg, 1992 ) </li></ul></ul><ul><ul><li>hybrid </li></ul></ul>
  4. 4. Collaborative Filtering (CF) <ul><li>In the real world we seek advices from our trusted people </li></ul><ul><li>CF automate the process of “word-of-mouth” </li></ul><ul><li>General use: </li></ul><ul><ul><li>Weight all users with respect to similarity with the active user. </li></ul></ul><ul><ul><li>Select a subset of the users ( neighbors ) to use as predictors (recommenders). </li></ul></ul><ul><ul><li>Rating prediction: </li></ul></ul>
  5. 5. User-User Collaborative Filtering ? 3 Active user Rating prediction
  6. 6. CF Limitation <ul><li>New item problem </li></ul><ul><li>Cold start problem </li></ul><ul><li>Sparsity (95%-99%) </li></ul><ul><li>Controversial user </li></ul><ul><li>Easy to attacks </li></ul><ul><li>Scalability </li></ul><ul><li>Cannot recommend items to someone with unique tastes. </li></ul><ul><ul><li>Tends to recommend popular items </li></ul></ul>
  7. 7. Solution: using trust relationships <ul><li>Implicit : Deriving trust score directly from the rating data </li></ul><ul><ul><li>Generally based on user prediction accuracy in the past </li></ul></ul><ul><li>Explicit : users explicitly “rate” other users </li></ul><ul><ul><li>FilmTrust (Hendler et al,2006) </li></ul></ul><ul><ul><li>Molskiing (Massa et al,2005) </li></ul></ul><ul><li>Limitation : </li></ul><ul><ul><li>Users have on average very few links (trusted sources) </li></ul></ul><ul><ul><li>More User’s effort </li></ul></ul><ul><li>Solution </li></ul><ul><ul><li>Trust propagation : find unknown user’s trustworthiness based on the users’ “web of trust” </li></ul></ul>
  8. 8. Trust inference <ul><li>Global metrics: computes a single global trust value for every single user (reputation) </li></ul><ul><li>Examples: </li></ul><ul><ul><li>PageRank (Page et al, 1998),eBuy </li></ul></ul><ul><li>Pros: </li></ul><ul><ul><li>Based on the whole community opinion </li></ul></ul><ul><ul><li>Simple to compute </li></ul></ul><ul><li>Cons: </li></ul><ul><ul><li>Trust is subjective (controversial users) </li></ul></ul>a b d c 1 5 3 2 3
  9. 9. Local trust metrics <ul><li>Local metrics: predicts (different) trust scores that are personalized from the point of view of every single user </li></ul><ul><li>Example: </li></ul><ul><ul><li>MoleTrust (Massa et al,2006) </li></ul></ul><ul><ul><li>TidalTrust (Golbeck et al,2005) </li></ul></ul><ul><li>Pros: </li></ul><ul><ul><li>More accurate </li></ul></ul><ul><ul><li>Attack resistance </li></ul></ul><ul><li>Cons: </li></ul><ul><ul><li>Ignoring the “wisdom of the crowd” </li></ul></ul><ul><ul><li>More complicated </li></ul></ul>a b d c 1 5 3 2 ?
  10. 10. Related works(1):Massa et al(2006) <ul><li>Crawling Epinion.com </li></ul><ul><li>users can review items and also assign them numeric ratings in the range 1 to 5. </li></ul><ul><li>Users can also express their “Web of Trust” and their Black list </li></ul><ul><li>Dataset: </li></ul><ul><ul><li>~50K users,~140K items,~665K reviews </li></ul></ul><ul><ul><li>487K binary trust statement </li></ul></ul><ul><ul><li>Sparsity=99.99135% </li></ul></ul><ul><li>Above 50% are cold start users (less than 5 review) </li></ul>
  11. 11. Recommendation method <ul><li>Using MoleTrust metric </li></ul>Estimated trust userXuser Predicted Ratings MXN Rating predictor Rating MXN Input output
  12. 12. Evaluation and results
  13. 13. Related works(2):Golbeck et al(2006) <ul><li>FilmTrust: Online Recommender System </li></ul><ul><li>Users can rate films, write reviews, and express trust statements in other users based on how much they trust their friends about movies ratings </li></ul><ul><li>Rating scale from half start to four start </li></ul><ul><li>Trust scale from 1 to 10 </li></ul><ul><li>Dataset: </li></ul><ul><ul><li>500 users, 100 popular movies, 11,250 rating </li></ul></ul><ul><ul><li>350 users with social connection </li></ul></ul><ul><ul><li>Sparsity=77% </li></ul></ul>
  14. 14. Recommendation method <ul><li>Weight ratings by trust value </li></ul><ul><li>Search recursively for trusted sources </li></ul><ul><li>Using TidalTrust metric for trust inference </li></ul><ul><li>Simple Prediction method </li></ul><ul><li>Example: </li></ul><ul><ul><ul><ul><li>Alice trust Bob 9 </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Alice trust Chuck 3 </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Bob rates the movie “Jaws” with 4 stars </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Chuck rates the movie “Jaws” with 2 stars </li></ul></ul></ul></ul><ul><li>Alice’s predicted rating for “Jaws” is: (9*4+3*2)/9+3=3.5 </li></ul>
  15. 15. Evaluation and results <ul><li>Benchmarks: Pure CF and simple average </li></ul><ul><li>80% training and 20% testing </li></ul><ul><li>Using MAE metric </li></ul><ul><li>First analysis, using trust didn’t appear to be effective </li></ul><ul><ul><li>Above 50% of the rating were within the range of the mean +/- half star </li></ul></ul><ul><li>Trust-based significantly useful only to user who disagree with the average </li></ul>
  16. 16. Result
  17. 17. Limitations <ul><li>Do not distinguish between various types of social relationships </li></ul><ul><li>Researches in marketing and in applied psychology identified different types of social measures impact recipient’s advice taking </li></ul><ul><li>Different types of social relations impact recipient’s advice taking in different ways </li></ul>
  18. 18. Dominants Social Measures <ul><li>Cognitive similarity (Gilly et al. 1998) </li></ul><ul><li>Tie-Strength (Levin & Cross 2004) </li></ul><ul><ul><li>Relationship duration </li></ul></ul><ul><ul><li>Interaction frequency </li></ul></ul><ul><ul><li>Closeness </li></ul></ul><ul><li>Trust (Smith et al. 2005) </li></ul><ul><ul><li>Competence </li></ul></ul><ul><ul><li>Benevolence </li></ul></ul><ul><ul><li>Integrity </li></ul></ul><ul><li>Social Capital/Reputation (Gilly et al. 1998) </li></ul>
  19. 19. Motivation <ul><li>Web 2.0 provide opportunity for peoples to interact with each other </li></ul><ul><ul><li>Social networks (trust, friendships) </li></ul></ul><ul><ul><li>Electronic communications (Tie-Strength) </li></ul></ul><ul><ul><li>Reputation mechanisms (Social Capital) </li></ul></ul>
  20. 20. Research questions <ul><li>Can additional relationship information be utilized to enhance recommender system performance? </li></ul><ul><li>What types of social relation is most useful? </li></ul>
  21. 21. Objectives <ul><li>Identify the difference between similarity based CF and social based CF </li></ul><ul><li>Explore the contribution of various social relations </li></ul><ul><li>Suggest solution for the cold start problem </li></ul><ul><li>Suggest solution for the scalability problem </li></ul>
  22. 22. Hypothesis <ul><li>H1 :Null Hypothesis: social relationships don’t provide any contribution to the performance of recommender systems </li></ul><ul><li>Alternative Hypothesis : social relationships do contribute to the performance of recommender systems </li></ul><ul><li>H2 :Null Hypothesis: different social relationships provide different contribution to the performance of recommender systems. </li></ul><ul><li>Alternative Hypothesis : different social relationships provide similar contribute to the performance of recommender systems </li></ul><ul><li>H3 :Null Hypothesis: different social relationships provide different contribution to the performance of recommender systems. </li></ul><ul><li>Alternative Hypothesis : different social relationships provide similar contribute to the performance of recommender systems </li></ul>
  23. 23. Social dimensions and measurement Measurement Social dimension This person is reputable Social capital How long have you known this person Relationship Duration How often did you communicate with this person Interaction Frequency I would consider this person a friend Friendship I trust this person Trust
  24. 24. Research Method <ul><li>Domain: movie recommendation </li></ul><ul><li>Subject : 97 4th years student from the IS department (with social relationships) </li></ul><ul><li>Tasks: </li></ul><ul><ul><li>Provide rating for 160 (popular) items (5 point scale) </li></ul></ul><ul><ul><li>Select three subject and indicate your social relationships </li></ul></ul><ul><li>Some of the relationships we examined </li></ul><ul><ul><ul><li>Trust </li></ul></ul></ul><ul><ul><ul><li>Friendship </li></ul></ul></ul><ul><ul><ul><li>Interaction duration </li></ul></ul></ul><ul><ul><ul><li>Interaction frequency </li></ul></ul></ul><ul><ul><ul><li>Reputation </li></ul></ul></ul>
  25. 25. Research method
  26. 26. Experiment Environment User Authentication Task1: Movies rating Task2: User's social relationships
  27. 27. Research framework Recipient-Source similarity Past Ratings Recipient Sources Systems Prediction Component System’s Prediction (Recommendation) System’s Receiver-Source Similarity Calculation System’s Source Qualification Component (Recipient’s) Sources’ Qualifications Reputation Trust, Friendship Interaction duration, frequency
  28. 28. Prediction method 1 <ul><li>Hybrid method </li></ul><ul><ul><li>Social relations combined with similarity (Pearson Correlation) </li></ul></ul><ul><ul><li>Tuning the source’s weight according to his group </li></ul></ul><ul><ul><li>Group P: sources similar to the active user </li></ul></ul><ul><ul><li>Group S: sources belong to the social network of the active user </li></ul></ul>P S
  29. 29. Prediction method 2 <ul><li>Social restriction </li></ul><ul><ul><li>Social relations used for restriction </li></ul></ul><ul><ul><li>Consider only sources belong to both groups S and P </li></ul></ul><ul><ul><li>Using the source’s similarity </li></ul></ul>P S
  30. 30. Social-based Prediction <ul><li>Prediction item i to user u </li></ul>
  31. 31. Simulation System Architecture
  32. 32. Results (Hybrid method)
  33. 33. Hybrid method: Cold start users
  34. 34. Impact of different social measures 9.497685 0.74192 0.797383 0.744079 Social capital 9.7585475 0.746152 0.795456 0.741934 Integrity 10.474944 0.744768 0.795776 0.736044 benevolence 10.1849155 0.741061 0.797798 0.738428 competence 10.18684322 0.743428 0.796603 0.738412 Trust 8.852814 0.752611 0.794472 0.74938 Closeness 8.979698 0.750318 0.795426 0.748337 interaction frequency 9.65368 0.746972 0.796085 0.742796 relationship duration 9.162064 0.749967 0.795328 0.746838 Tie-Strength   0.731773 0.798522 0.822165 Cognitive similarity Improvements Recall Precision AMAE Social measures
  35. 35. Result (Social restriction)
  36. 36. Social restriction: cold start users
  37. 37. Conclusion <ul><li>Social relationships is effective in alleviating CF weaknesses: </li></ul><ul><ul><li>Cold start problem (Social weighting and social restriction) </li></ul></ul><ul><ul><li>Scalability problem (Social restriction) </li></ul></ul><ul><ul><li>Spammers attacks (Social weighting and social restriction) </li></ul></ul>
  38. 38. References <ul><li>Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating ’Word of Mouth’. In: Proceedings of Human Factors in Computing Systems, pp.10–217 (1995) </li></ul><ul><li>Herlocker, J., Konstan, J.A., Terveen, L., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22, 5–53(2004) </li></ul><ul><li>Massa, P., Avesani, P.: Trust-Aware Collaborative Filtering for Recommender Systems.In: Proceedings of the International Conference on Cooperative Information Systems (CoopIS), Agia Napa, Cyprus, pp. 492–508 (2004)1060 C.-S. Hwang and Y.-P. Chen </li></ul><ul><li>Avesani, P., Massa, P., Tiella, R.: Moleskiing: A Trust-Aware Decentralized Recommender System. In: Proceedings of the First Workshop on Friend of a FriendSocial Networking and the Semantic Web, Galway, Ireland (2004) </li></ul><ul><li>Golbeck, J: Generating Predictive Movie Recommendations from Trust in Social Networks. Proceedings of the Fourth International Conference on Trust Management. Pisa, Italy, May 2006. </li></ul><ul><li>R. Guha, R. Kumar, P.:Raghavan, and A. Tomkins. Propagation of trust and distrust. In Proc. of the Thirteenth International World Wide Web Conference, MAY 2004. </li></ul>

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