Improving recommendation accuracy by clustering social networks with trust

Abstract

Social trust relationships between users in social networks speak to the similarity in opinions between the users, both in general and in important nuanced ways. They have been used in the past to make recommendations on the web. New trust metrics allow us to easily cluster users based on trust. In this paper, we investigate the use of trust clusters as a new way of improving recommendations. Previous work on the use of clusters has shown the technique to be relatively unsuccessful, but those clusters were based on similarity rather than trust. Our results show that when trust clusters are integrated into memory-based collaborative filtering algorithms, they lead to statistically significant improvements in accuracy. In this paper we discuss our methods, experiments, results, and potential future applications of the technique.