A Two-Stage Rating Prediction Approach Based on Matrix Clustering on Implicit Information

Abstract

Traditional matrix factorization (MF) methods take a global view on the user-item rating matrix to conduct matrix decomposition for rating approximation. However, there is an inherent structure in the user-item rating matrix and a local correspondence between user clusters and item clusters as the users induce the items and the items imply the users in a recommendation system. This article proposes a novel approach called two-stage rating prediction (TS-RP) to matrix clustering with implicit information. In the first stage, implicit feedback is used to discover the inherent structure of the user-item rating matrix by spectral clustering. In the second stage, we conduct rating prediction on the dense blocks of explicit information of user-item clusters discovered in the first stage. The proposed TS-RP approach can not only alleviate the data sparsity problem in recommendation but also increase the computation scalability. Experiments on the MovieLens-100K data set demonstrate that the proposed TS-RP approach performs better than most state-of-the-art methods of rating prediction based on MF in terms of recommendation accuracy and computation complexity.