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Modeling User Preference from Rating Data Based on the Bayesian Network with a Latent Variable

  • Renshang Gao
  • Kun YueEmail author
  • Hao Wu
  • Binbin Zhang
  • Xiaodong Fu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)

Abstract

Modeling user behavior and latent preference implied in rating data are the basis of personalized information services. In this paper, we adopt a latent variable to describe user preference and Bayesian network (BN) with a latent variable as the framework for representing the relationships among the observed and the latent variables, and define user preference BN (abbreviated as UPBN). To construct UPBN effectively, we first give the property and initial structure constraint that enable conditional probability distributions (CPDs) related to the latent variable to fit the given data set by the Expectation-Maximization (EM) algorithm. Then, we give the EM-based algorithm for constraint-based maximum likelihood estimation of parameters to learn UPBN’s CPDs from the incomplete data w.r.t. the latent variable. Following, we give the algorithm to learn the UPBN’s graphical structure by applying the structural EM (SEM) algorithm and the Bayesian Information Criteria (BIC). Experimental results show the effectiveness and efficiency of our method.

Keywords

Rating data User preference Latent variable Bayesian network Structural EM algorithm Bayesian information criteria 

Notes

Acknowledgements

This paper was supported by the National Natural Science Foundation of China (Nos. 61472345, 61562090, 61462056, 61402398), Natural Science Foundation of Yunnan Province (Nos. 2014FA023, 2013FB009, 2013FB010), Program for Innovative Research Team in Yunnan University (No. XT412011), and Program for Excellent Young Talents of Yunnan University (No. XT412003).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Renshang Gao
    • 1
  • Kun Yue
    • 1
    Email author
  • Hao Wu
    • 1
  • Binbin Zhang
    • 1
  • Xiaodong Fu
    • 2
  1. 1.Department of Computer Science and Engineering, School of Information Science and EngineeringYunnan UniversityKunmingChina
  2. 2.Faculty of Information Engineering and AutomationKunming University of Science and TechnologyKunmingChina

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