Abstract
Collaborative Filtering (CF) Systems have been studied extensively for more than a decade to confront the "information overload" problem. Nearest-neighbor CF is based either on common user or item similarities, to form the user's neighborhood. The effectiveness of the aforementioned approaches would be augmented, if we could combine them. In this paper, we use biclustering to disclose this duality between users and items, by grouping them in both dimensions simultaneously. We propose a novel nearest-biclusters algorithm, which uses a new similarity measure that achieves partial matching of users' preferences. We apply nearest-biclusters in combination with a biclustering algorithm - Bimax - for constant values. Extensively performance evaluations on two real data sets is provided, which show that the proposed method improves the performance of the CF process substantially. We attain more than 30% and 10% improvement in terms of precision and recall, respectively. © Springer-Verlag Berlin Heidelberg 2007.