SVD free matrix completion with online bias correction for Recommender systems

In this work we address the problem of design of an efficient Recommender system based on collaborative filtering framework which achieves improved accuracy with reduced computational complexity and shorter run times. This work is based on representing the low rank constraint as the Ky-Fan norm instead of the commonly employed nuclear norm term. Our formulation uses majorization minimization approach to cast the problem as simple least squares. The enhanced efficiency of our algorithm in terms of higher accuracy of recovery and shorter execution times is demonstrated by comparison to existing techniques for matrix completion.