Listwise Recommendation Approach with Non-negative Matrix Factorization

Abstract

Matrix factorization (MF) is one of the most effective categories of recommendation algorithms, which makes predictions based on the user-item rating matrix. Nowadays many studies reveal that the ultimate goal of recommendations is to predict correct rankings of these unrated items. However, most of the pioneering efforts on ranking-oriented MF predict users’ item ranking based on the original rating matrix, which fails to explicitly present users’ preference ranking on items and thus might result in some accuracy loss. In this paper, we formulate a novel listwise user-ranking probability prediction problem for recommendations, that aims to utilize a user-ranking probability matrix to predict users’ possible rankings on all items. For this, we present LwRec, a novel listwise ranking-oriented matrix factorization algorithm. It aims to predict the missing values in the user-ranking probability matrix, aiming that each row of the final predicted matrix should have a probability distribution similar to the original one. Extensive offline experiments on two benchmark datasets against several state-of-the-art baselines demonstrate the effectiveness of our proposal.