Multi-criteria recommendation schemes based on factorization machines

Traditional collaborative filtering (CF) recommendation algorithms usually use a single rating to recommend items to users, which works well in terms of predictive accuracy. However, recent research on multi-criteria recommender has shown that multi-criteria ratings are of great value to improving recommendation performance. In this paper, we present novel multi-criteria recommendation schemes which leverage multi-criteria ratings and codebook cluster information derived from user-item-criteria ratings matrix to enhance recommendation quality. Particularly, we utilize Factorization Machines (FMs) to integrate the codebook clusters information on individual criteria, which contains users' preferences on different criteria of items, to extend user-item-criteria interaction feature vectors and make an overall rating prediction. A set of experiments on a real-world datasets show that our approach outperforms both FMs-based single-rating recommendation algorithms in which the clusters information of users or items are based on an overall rating, as well as three existing state-of-the-art multi-criteria recommendation algorithms even in case where data are under high sparsity.