JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (3): 65-73.doi: 10.6040/j.issn.1672-3961.2.2015.080

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Multi-criteria collaborative filtering algorithm based on probabilistic matrix factorization

PANG Juntao1, ZHANG Hui2*, YANG Chunming1, LI Bo1,3, ZHAO Xujian1   

  1. 1.School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China;
    2. Educational Informationization Office, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China;
    3. School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, Anhui, China
  • Received:2015-06-23 Online:2016-06-30 Published:2015-06-23

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Abstract: In order to solve the problem that the correlation was neglected among the multi-criteria in the recommendation method of the multi-criteria, a multi-criteria collaborative filtering algorithm based on probabilistic matrix factorization(MCPMF)was proposed. The algorithm represented the multi-criteria as a weight-matrix which has influence on all users and items. The latent distribution of the weight-matrix was assumed to follow Gaussian distribution, and the probability density distribution of the matrix was conditional related to the distribution of user and item latent feature matrix. The user and item feature matrix was learned by probability matrix factorization method. Experimental results on two real datasets showed that the proposed method was more accurate in forecasting the user's overall rating compared with methods which only considered single overall rating and could reduce the impact of data sparsity to recommendation algorithms.

Key words: probabilistic matrix factorizetion, collaborative filtering, recommendation system, multi-criteria

CLC Number: 

  • TP181
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