Improved Collaborative Filtering Recommendation Through Similarity Prediction

Abstract

Collaborative Filtering (CF) approaches have been widely used in various applications of recommender systems. These methods are based on estimating the similarity between users/items by analyzing the ratings provided by users. The existing methods are often domain-specific and have not considered the time of the ratings being made in the calculation of the similarity. However, users' preferences vary over time, and so their similarity. In this paper, a novel method is proposed by re-ranking the users/items neighborhood set considering their future similarity trend. The trend of similarity is predicted, and depending on increased/decreased trend, we update the final nearest neighbor sets that are used in CF formulation. This method can be applied on a broad range of CF methods that are based on similarities between users and/or items. We apply the proposed approach on a set of CF algorithms over two benchmark datasets and show that the proposed approach significantly improves the performance of the original CF recommenders. As the proposed method only re-ranks the neighborhood set, it can be applied to any existing non-temporal similarity-based CF recommenders to improve their performance.