GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
IEEE Transactions on Knowledge and Data Engineering
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Currently, most recommender systems are using collaborative filtering (CF) techniques. The main idea is to suggest new relevant items for an active user based on the judgements from other members in the like-minded community. However, these CF-based methods encounter the obstacles, such as sparse data, cold-start and robustness. This paper proposes to deal with these issues by associating similarity measurement from users' rating patterns with trust metric. After investigating the large data set from Epinions.com, we find that user similarity and trust are strongly correlated. This fact also explains why using trust (mstead of user similarity) could lead to very close mean prediction accuracy in a Pearson Correlation Coefficient-like recommendation algorithm. Our novel method incorporates these two factors into one unified recommendation algorithm. The experimental results indicate that a good prediction strategy can come from filtering the ratings from the users who have high trust and low similarity or vice versa.