Nearest-Biclusters Collaborative Filtering

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. Performance evaluation results are offered, which show that the proposed method improves substantially the performance of the CF process. We attain more than 30% and 10% improvement in terms of precision and recall, respectively.