Towards comprehensive approaches for the rating prediction phase in memory-based collaborative filtering recommender systems

Abstract

Recommender systems play an indispensable role in today’s online businesses. In these systems, memory-based (neighborhood-based) collaborative filtering is an important strategy to predict items as expected by users. It consists of two phases: computing the preference similarity between each pair of users in the offline phase and predicting the rating of an active user for a target item in the online phase by aggregating ratings of his/her neighbors for the target item. Previous studies on memory-based collaborative filtering have heavily concentrated on proposing methods for the computation of user preference similarity. To further improve the performance of memory-based collaborative filtering, this paper is aimed at the rating prediction phase. By optimizing a proposed objective function, the method we used in the rating prediction phase helps more accurately estimate the weight between the active user and each of his/her neighbors. The experimental results show that the proposed method outperforms others, especially in the case of a small and medium number of selected neighbors.