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

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"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,
http://www.wprrs.scitech.qut.edu.au/
Università degli Studi di Milano Bicocca, Milano, Italy
September 15–18, 2009

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  • 25 minutes = 20 minutes pres. + 5 minutes questions.
  • CF algorithm generally make recommendations based on similarity between users. As mentioned, similarity measure is not sufficient when user profiles are sparse. A connection between how similar two users are and how much they trust each other, is discussed trust can be considered as a measure for expressing the relationship between two users in recommendation systems They depict that trust can be aggregated for all of the users in a social network, and the “importance”of a certain user is predicated by using a graph walking algorithm
  • Coverage measures the percentage of items that a recommender system can provide predictions for
  • Mean Absolute Error (MAE) measures the average absolute difference between a predicted rating made for a specific user and the user’s actual rating
  • Distribution of Indegree for the first ten of most trustworthy users with and without T-index. Indegree depicts incoming edges to a node as a user who is trusted by others. It is observed that by employing T-index, more centric users can be found which results in more clusters . Therefore, node’s weights in terms of incoming trust relationships are more balanced in trust graph of users, by utilizing Tindex.
  • 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>http://www.movielens.org/ </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>http://www.isk.kth.se/~zarghami/ </li></ul></ul><ul><li>Soude Fazeli </li></ul><ul><ul><li>http://www.isk.kth.se/~soude/ </li></ul></ul><ul><li>Nima Dokoohaki </li></ul><ul><ul><li>http://web.it.kth.se/~nimad/ </li></ul></ul><ul><li>Misha Matskin </li></ul><ul><ul><li>http://www.idi.ntnu.no/~misha/ </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 : nimad@kth.se </li></ul><ul><li>http://web.it.kth.se/~nimad/ </li></ul>Presenter

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