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