A Novel Recommender System using Hidden Bayesian Probabilistic Model based Collaborative Filtering

Xin Dai, Fanzhang Li, Xiaopei Li, Helan Liang

Abstract: For the problems of data sparseness and cold start of goods in the existing recommendation algorithm, in this paper, we propose a new method based on the hidden Bayesian method to predict user preferences. Our approach is to use the variational Bayesian non-negative matrix factorization on observable rating matrices (users- items), which can predict the filling of the scoring matrix and cluster the users. On this basis, the user's hidden information and pre-rating are obtained, and the pre-rating is corrected in combination with the item attribute information by improved naive Bayes classifier. Experimental results show that 1) this method does not require additional clustering algorithms, which saves execution time. 2) Compared with the classical matrix factorization and similarity algorithm, our method solves the cold start problem of new items and the greatly improved the accuracy of recommendation.