Clustering for collaborative filtering applications

Collaborative ltering systems assist users to identify items of interest by providing predictions based on ratings of other users. The quality of the predictions depends strongly on the amount of available ratings and collaborative ltering algorithms perform poorly when only few ratings are available. In this paper we identify two important situations with sparse ratings: Bootstrapping a collaborative ltering system with few users and providing recommendations for new users, who rated only few items. Further, we present a novel algorithm for collaborative ltering, based on hierarchical clustering, which tries to balance robustness and accuracy of predictions, and experimentally show that it is especially e cient in dealing with the previous situations.