Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Social Trust-aware Recommendation System: A T-Index Approach


Published on

"Social Trust-aware Recommendation System: A T-Index Approach"
Workshop on Web Personalization, Reputation and Recommender Systems (WPRRS09)
Held in conjunction with 2009 IEEE/ WIC/ ACM International Conference on Web Intelligence (WI 2009) and Intelligent Agent Technology,
Università degli Studi di Milano Bicocca, Milano, Italy
September 15–18, 2009

Published in: Technology, Business
  • Be the first to comment

Social Trust-aware Recommendation System: A T-Index Approach

  1. 1. Social Trust-Aware Recommendation System: A T-index Approach Alireza Zarghami Soude Fazeli Nima Dokoohaki Mihhail Matskin Presented at Workshop on Web Personalization, Reputation and Recommender Systems ( WPRRS’09 ) In conjunction with IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology ( WI’09 and IAT’09). September 2009
  2. 2. <ul><li>Motivation </li></ul><ul><li>Contribution </li></ul><ul><ul><li>T-Index + TopTrustee </li></ul></ul><ul><li>Approach </li></ul><ul><ul><li>Framework </li></ul></ul><ul><ul><li>Ontologies </li></ul></ul><ul><ul><li>Trust Calculation </li></ul></ul><ul><ul><li>Metric Choice </li></ul></ul><ul><ul><li>Trust Transposure </li></ul></ul><ul><ul><li>Trust Propagation </li></ul></ul><ul><ul><li>Recommendation Prediction </li></ul></ul><ul><li>Experiment </li></ul><ul><ul><li>Coverage, MAE, Indegree </li></ul></ul><ul><li>Conclusion </li></ul>Agenda
  3. 3. <ul><li>Memory-based </li></ul><ul><ul><li>Utilize the entire user-item data to predict likeness; </li></ul></ul><ul><ul><li>NNR, Pearson, statistical approach </li></ul></ul><ul><li>Model-Based </li></ul><ul><ul><li>Clustering, Bayesian, Rule based, Probabilistic Approach </li></ul></ul><ul><li>Trust-Based √ </li></ul><ul><ul><li>Correlation between trust and similarity (proved by Golbeck, Massa/Avesani) </li></ul></ul>Collaborative Filtering heuristics
  4. 4. <ul><li>Model a recommendation system </li></ul><ul><ul><li>Utilizes a distributed trust-based CF </li></ul></ul><ul><ul><li>Utilizes Semantic Web Ontology to deal with heterogeneous networks of users and items </li></ul></ul><ul><ul><li>Ability to traverse the trust networks to collect Recommendations </li></ul></ul><ul><ul><li>To have better coverage and prediction accuracy in short traversal by optimizing the trust network maintenance mechanism </li></ul></ul>Contribution
  5. 5. <ul><li>Our work is based upon two main ideas: </li></ul><ul><li>T-index </li></ul><ul><ul><li>A measure inspired by Hindex to discover the agents within our trust network who provide trust values higher or equal to T. </li></ul></ul><ul><li>TopTrustee </li></ul><ul><ul><li>A list, which provides information about users who might not be accessible within a predefined maximum path length. </li></ul></ul><ul><ul><li>TopTrustee List= </li></ul></ul><ul><ul><li>(m) raters who provide Highest T-index values. </li></ul></ul>TopTrustee/ T-index
  6. 6. TopTrustee Idea Depicted <ul><li>An example of TopTrustee </li></ul><ul><li>Finding trustworthy users across the trust network even outside the traversal path length limit </li></ul>
  7. 7. T-index Idea Depicted <ul><li>An example of T-index </li></ul><ul><li>Indegree (Ua) = 7 </li></ul><ul><li>Indegree (Ub) = 5 </li></ul><ul><li>T-index (Ua) = 2 </li></ul><ul><li>T-index (Ub) = 4 </li></ul>
  8. 8. Ontological Model <ul><li>Framework can deal with heterogeneous networks of user and item in a distributed manner </li></ul><ul><li>Users from different groups can be hosted by different servers possibly located in different organization for sake of privacy, accessible by their URI </li></ul>
  9. 9. User Ontology <ul><li>Relationship: </li></ul><ul><ul><li>Top-n Trustees </li></ul></ul><ul><li>Rank Relation: </li></ul><ul><ul><li>History of rating </li></ul></ul><ul><li>T-index: </li></ul><ul><ul><li>User's T-index </li></ul></ul>
  10. 10. Item Ontology <ul><ul><li>Ontological Item Profile </li></ul></ul>
  11. 11. <ul><li>Choice of trust metric </li></ul><ul><ul><li>(common case) Trust value defined as a decimal value [0,1] </li></ul></ul><ul><li>For users who find each other through TopTrustee list, calculated directly based on their common item in two steps: </li></ul><ul><ul><li>Transpose their values to have same rating scale </li></ul></ul><ul><ul><li>Calculate their mutual Trust </li></ul></ul>Trust Metric and Calculation
  12. 12. Transposure of Trustee Rating <ul><li>Different users have different scales for rating, </li></ul><ul><li>Row : Truster </li></ul><ul><li>Column : Trustee </li></ul><ul><li>tr(5)=4.43 Trustee' rating of 5 is considered as 4.43 for Truster to calculate trust </li></ul>
  13. 13. <ul><li>After transposing rating values of trustee to the same scale rating of truster, we compute their mutual trust value based on this formula* </li></ul><ul><li>Formula calculates the sum of their differences in rating values for common items divided by the number of truster's item multiplied by the maximum rating value. </li></ul><ul><ul><li>* N. Lathia, S. Hailes and L. Capra. “Trust-based collaborative </li></ul></ul><ul><ul><li>filtering”, in IFIPTM 2008: Joint iTrust and PST Conferences on </li></ul></ul><ul><ul><li>Privacy, Trust Management and Security, P.14, London, 2008. </li></ul></ul>Trust Computation
  14. 14. <ul><li>Basic approach </li></ul><ul><li>For users who has no direct trust relationship, we propagate trust by multiplying trust values of the nodes are located in the path between them. </li></ul>Trust Propagation
  15. 15. Collecting Recommendation for users <ul><li>Recommendations for a particular user are collected by asking from its direct or indirect neighbors through traverals. </li></ul><ul><li>Limiting traversal length </li></ul><ul><ul><li>Trust threshold √ </li></ul></ul><ul><ul><li>Path length </li></ul></ul><ul><li>For instance: </li></ul><ul><li>Um is more trustworthy than Ug for Ua </li></ul>
  16. 16. Recommendation – traversal path length <ul><li>we just collect recommendations from short traversal length , so all traversals are limited to a predefined maximum traversal path length. </li></ul><ul><li>If the maximum defined as 3, traversal can not go further than Um regardless of trust value </li></ul>
  17. 17. Recommendation Prediction <ul><li>Prediction of the Recommendations collected from direct or indirect neighbors are done by the weighted average of their rating based on their trust values calculated either through computation or propagation </li></ul>
  18. 18. <ul><li>MovieLens </li></ul><ul><ul><li> </li></ul></ul><ul><li>100,000 rating of 5-point scale </li></ul><ul><li>943 users and 1682 movies </li></ul><ul><li>Rating are sorted according to their timestamps </li></ul><ul><li>80% of rating used to build the network </li></ul><ul><li>20% of rating used to test the recommendations </li></ul>Experiment - Dataset
  19. 19. <ul><li>Parameters </li></ul><ul><ul><li>N number of neighbors per user (2,3,5,10,20,50) </li></ul></ul><ul><ul><li>M number of TopTrustee per item (2,3,5,7) </li></ul></ul><ul><ul><li>With or without T-index (0,100) </li></ul></ul><ul><ul><li>Trust threshold is defined as 0.1 </li></ul></ul><ul><ul><li>Maximum path length of traversal is defined as 3 </li></ul></ul><ul><li>Experiments </li></ul><ul><ul><li>MAE </li></ul></ul><ul><ul><li>Coverage </li></ul></ul><ul><ul><li>Indegree distribution of most trustworthy users </li></ul></ul>Experiment Types / Parameters
  20. 20. Coverage
  21. 21. MAE
  22. 22. Indegree distribution most trustworthy users
  23. 23. Trust network visualization Configuration: n=3 m=3 T-index=0
  24. 24. Trust network visualization Configuration: n=3 m=3 T-index=100
  25. 25. <ul><li>Designed an ontological model to model heterogeneous networks of users and items </li></ul><ul><li>Introduced TopTrustee list to enhance the process of discovering neighbors </li></ul><ul><li>Introduced T-index as a measure of trustworthiness </li></ul><ul><ul><li>which can improve the Coverage and MAE in short traversal path </li></ul></ul><ul><ul><li>length, especially for small size of neighbors </li></ul></ul><ul><li>T-index can improve trust network structure by increasing the number of well connected clusters </li></ul>Conclusion
  26. 26. <ul><li>Thanks  </li></ul><ul><li>Contacts: </li></ul><ul><li>----------- </li></ul><ul><li>Alireza Zarghami </li></ul><ul><ul><li> </li></ul></ul><ul><li>Soude Fazeli </li></ul><ul><ul><li> </li></ul></ul><ul><li>Nima Dokoohaki </li></ul><ul><ul><li> </li></ul></ul><ul><li>Misha Matskin </li></ul><ul><ul><li> </li></ul></ul>Questions
  27. 27. <ul><li>Nima Dokoohaki </li></ul><ul><li>Software and Computer Systems ( SCS ), Department of Electronics ,Computer and </li></ul><ul><li>Software Systems ( ECS ), School of Information and Communications Technology ( ICT ), Royal Institute of Technology ( KTH ), Stockholm, Sweden Office: +46 (0) 8 790 4149 Cell : +46 (0) 76 269 76 30 Fax: +46 (0) 8 751 1793 Email : </li></ul><ul><li> </li></ul>Presenter