exploiting user similarity based on rated-item pools for improved user-based collaborative filtering
An approach to user-based collaborative filtering is proposed that refines prediction of item ratings that is based on global user similarity by incorporating information derived from a more detailed user comparison made on the basis of Rated Item Pools (RIPs). The preference spectrum defined by items that a user has rated, and ranging from best-liked to most disliked items, is divided into item sets, or RIPs, which supply the basis for a fine-grained calculation of similarity between users. The RIP-based approach makes it possible for the model to take advantage of user tastes that are matched at one end of the spectrum, e.g., two users agree on favorites, without requiring complete correspondence of item ratings between user profiles. The approach improves rating prediction, as compared to a baseline that uses the global user similarity alone. It does not unduly inflate computational complexity or rely on external resources, common shortcomings of competing rating prediction methods. Cases in which the nearest neighbors are relatively dissimilar, known to be challenging for user-based collaborative filtering, demonstrate particularly substantial improvement. Performance is shown to be stable across the choice of neighborhood size, number of pools and relative pool size.
collaborative filtering user-item matrix recommendation recommender systems