Trust-aware Collaborative Filtering for Recommender Systems
Paolo Massa, Paolo Avesani
Published in International Conference on Cooperative Information Systems (CoopIS)
2004

Trust-aware Collaborative Filtering for Recommender Systems

Abstract
Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed “web of trust”, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation allows to increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improuvements are achieved for new users, who provided few ratings.

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