Using Trust in Collaborative Filtering Recommendation

Collaborative filtering (CF) technique has been widely used in recommending items of interest to users based on social relationships. The notion of trust is emerging as an important facet of relationships in social networks. In this paper, we present an improved mechanism to the standard CF techniques by incorporating trust into CF recommendation process. We derive the trust score directly from the user rating data and exploit the trust propagation in the trust web. The overall performance of our trust-based recommender system is presented and favorably compared to other approaches.

[1]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[2]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[3]  Stephen Hailes,et al.  Supporting trust in virtual communities , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[4]  Ravi Kumar,et al.  Recommendation Systems , 2001 .

[5]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[6]  Paolo Avesani,et al.  Trust-Aware Collaborative Filtering for Recommender Systems , 2004, CoopIS/DOA/ODBASE.

[7]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[8]  James A. Hendler,et al.  Reputation Network Analysis for Email Filtering , 2004, CEAS.

[9]  Georgios Pitsilis,et al.  A Model of Trust Derivation from Evidence for Use in Recommendation Systems , 2004 .

[10]  Bobby Bhattacharjee,et al.  Using Trust in Recommender Systems: An Experimental Analysis , 2004, iTrust.

[11]  Dimitris Plexousakis,et al.  Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences , 2005, iTrust.

[12]  Karl Pearson,et al.  Mathematical contributions to the theory of evolution. VIII. On the correlation of characters not quantitatively measurable , 1900, Proceedings of the Royal Society of London.

[13]  Georg Lausen,et al.  Analyzing Correlation between Trust and User Similarity in Online Communities , 2004, iTrust.

[14]  Barry Smyth,et al.  Trust in recommender systems , 2005, IUI.