Abstract：User preference is a latent variable that determines online product ratings.This paper is to construct the latent variable model with user preference，and describe arbitrary dependence relationships as well as the corresponding uncertainties in rating data by adopting Bayesian network as the preliminary framework.In this paper，we start from the rating data and construct the product rating model without latent variables at first.Then，we give the method for inserting latent variables based the semi-clique structure，so the model can be constructed to describe user preference by the inserted a latent variable.Following，we give the EM-algorithm based method for estimating parameters in the latent variable model.Finally，we propose the algorithm for probabilistic inferences of the latent variable model and the method for predicting user ratings.Experimental results on the MovieLens and Book-Crossing datasets show that our method is effective.
2017, Vol. 38 
