A Survey on Predicting User Service Rating in Social Network Using Data Mining Methods

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چکیده

Recommender system plays an important role in our daily life. Recommender systems naturally intimate items to users that might be fascinating for them. We propose probabilistic matrix factorization technique for recommendations. The (PMF) model is proposed which computes continuously with the number of investigations and, more specially, functions well on the massive, inadequate, and very uneven Netflix dataset. A user –service rating prediction approach is proposed by exploring the behavior of user’s rating in social network. The probabilistic matrix factorization is a model-based collaborative filtering approach. The Probabilistic Matrix Factorization algorithm integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. In the approach of user service rating prediction our factors are fused user personal interest interpersonal interest similarity interpersonal rating behavior similarity and interpersonal rating behavior diffusion into a unified matrix-factorized framework

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تاریخ انتشار 2017