Improving recommendation accuracy by clustering social networks with trust

Social trust relationships between users in social networksspeak 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 un-
successful, but those clusters were based on similarity rather
than trust. Our results show that when trust clusters are
integrated into memory-based collaborative filtering algo-
rithms, they lead to statistically significant improvements
in accuracy. In this paper we discuss our methods, experi-
ments, results, and potential future applications of the tech-
nique.