Matrix completion incorporating auxiliary information for recommender system design

Abstract

Rating prediction accuracy of latent factor analysis based techniques in collaborative filtering is limited by the sparsity of available ratings. Usually more than 90% of the missing ratings need to be predicted from less than 10% of available ratings. The problem is highly under-determined. In this work, we propose to improve the prediction accuracy by exploiting the user’s demographic information. We propose a new formulation to incorporate this information into the matrix completion framework of latent factor based collaborative filtering. The ensuing problem is efficiently solved using the split Bregman technique. Experimental evaluation indicates that the use of additional information indeed improves the accuracy of rating prediction. We also compared our proposed approach with an existing technique that incorporates auxiliary information using a graph-Laplacian framework and one utilizing neighborhood based approach; we find that our proposed method yields considerably superior results.