A Latent Variable Bayesian Network Recommendation Model for Product Scoring Prediction

Including scenes features, including the dependencies between the attribute information and user ratings are implicit variables determine a user score of commodities to build hidden variable model includes user preferences, describe any form of dependence between the rating data relevant attributes as the main target, The Bayesian network is used as the basic framework of the dependencies between attributes and the probability relationship between attributes and scores. The project scoring data is used to construct the item scoring model without hidden variables, and the semi-clique structure is proposed to insert the description of user preferences the latent variable model with user preference is constructed, and the method of parameter estimation of hidden variable model based on EM algorithm is given. Then the probability inference model of the hidden variable model and the corresponding project score prediction method are proposed. The experimental results based on MovieLens and LDOS-CoMoDa data show that the proposed Bayesian model with implicit variables and the corresponding score prediction method are effective.