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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Social Trust-aware Recommendation System: A T-Index Approach
- 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
- 20. Coverage
- 21. MAE
- 22. Indegree distribution most trustworthy users
- 23. Trust network visualization Configuration: n=3 m=3 T-index=0
- 24. Trust network visualization Configuration: n=3 m=3 T-index=100
Editor's Notes
- 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.