Pairwise Preferences Based Matrix Factorization and Nearest Neighbor Recommendation Techniques

Abstract

Many recommendation techniques rely on the knowledge of preferences data in the form of ratings for items. In this paper, we focus on pairwise preferences as an alternative way for acquiring user preferences and building recommendations. In our scenario, users provide pairwise preference scores for a set of item pairs, indicating how much one item in each pair is preferred to the other. We propose a matrix factorization (MF) and a nearest neighbor (NN) prediction techniques for pairwise preference scores. Our MF solution maps users and items pairs to a joint latent features vector space, while the proposed NN algorithm leverages specific user-to-user similarity functions well suited for comparing users preferences of that type. We compare our approaches to state of the art solutions and show that our solutions produce more accurate pairwise preferences and ranking predictions.