A Novel Probabilistic Matrix F actorization for Personal Recommendation

Abstract

Matrix Factorization (MF) is a well-known method which is to predict users’ preference in recommender systems. This method has the advantage of reducing data sparsity and cold start problem. Probabilistic matrix factorization (PMF) is an improved method of matrix factorization. Recommender system can efficiently reduce the information overload problem. However, it is bound by the recommended accuracy. In this paper we introduce Bayesian distribution to improve accuracy and design our novel probabilistic matrix factorization (NPMF) proved to have better performance. In addition, we also use K nearest neighbor method and stochastic gradient descent to further improve our method. The experimental studies using two different datasets including MovieLens 100k and MovieLens 1M, show that the proposed methods already have comparable and better performance.