A sub-one quasi-norm-based similarity measure for collaborative filtering in recommender systems

Abstract

Collaborative filtering (CF) is one of the most successful approaches for an online store to make personalized recommendations through its recommender systems. A neighborhood-based CF method makes recommendations to a target customer based on the similar preference of the target customer and those in the database. Similarity measuring between users directly contributes to an effective recommendation. In this paper, we propose a sub-one quasi-norm-based similarity measure for collaborative filtering in a recommender system. The proposed similarity measure shows its advantages over those commonly used similarity measures in the literature by making better use of rating values and deemphasizing the dissimilarity between users. Computational experiments using various real-life datasets clearly indicate the superiority of the proposed similarity measure, no matter in fully co-rated, sparsely co-rated or cold-start scenarios.