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. 数据挖掘研究院
