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)


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.


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



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).


  1. 1.
    Netica Application (2016).
  2. 2.
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  4. 4.
    Breese, J., Heckerman, D., Kadie, C.M.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI 1998, pp. 43–52. Morgan Kaufmann (1998)Google Scholar
  5. 5.
    Dempster, A., Laird, N., Rubin, D.: Maximum-likelihood from Incomplete Data via the EM algorithm. J. Royal Stat. Soc. 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Friedman, N.: Learning belief networks in the presence of missing values and hidden variables. In: ICML 1997, pp. 452–459. ACM (1997)Google Scholar
  7. 7.
    Friedman, N.: The Bayesian structural EM algorithm. In: UAI 1998, pp. 129–138. Morgan Kaufmann (1998)Google Scholar
  8. 8.
    Elidan, G., Lotner, N., Friedman, N., Koller, D.: Discovering Hidden variables: a structure-based approach. In: NIPS 2000, pp. 479–485 (2000)Google Scholar
  9. 9.
    Huang, Y., Bian, L.: A bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the internet. Expert Syst. Appl. 36(1), 933–943 (2009)CrossRefGoogle Scholar
  10. 10.
    Huete, J., Campos, L., Fernandez-luna, J.M.: Using structural content information for learning user profiles. In: SIGIR 2007, pp. 38–45 (2007)Google Scholar
  11. 11.
    Kim, J., Jun, C.: Ranking evaluation of institutions based on a bayesian network having a latent variable. Knowl. Based Syst. 50, 87–99 (2013)CrossRefGoogle Scholar
  12. 12.
    Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  13. 13.
    Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)CrossRefGoogle Scholar
  14. 14.
    Liu, T., Zhang, N.L., Chen, L., Liu, A.H., Poon, L., Wang, Y.: Greedy learning of latent tree models for multidimensional clustering. Mach. Learn. 98(1–2), 301–330 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Pearl, J.: Fusion, propagation, and structuring in belief networks. Artif. Intell. 29(3), 241–288 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Salakhutdinov, R., Mnih, A.: Probabilistic Matrix Factorization. In: NIPS 2007, pp. 1257–1264 (2007)Google Scholar
  17. 17.
    Tan, F., Li, L., Zhang, Z., Guo, Y.: A multi-attribute probabilistic matrix factorization model for personalized recommendation. In: Dong, X.L., Yu, X., Li, J., Sun, Y. (eds.) WAIM 2015. LNCS, vol. 9098, pp. 535–539. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-21042-1_57 CrossRefGoogle Scholar
  18. 18.
    Yin, H., Cui, B., Chen, L., Hu, Z., Huang, Z.: A Temporal context-aware model for user behavior modeling in social media systems. In: SIGMOD 2014, pp. 1543–1554. ACM (2014)Google Scholar
  19. 19.
    Yu, K., Zhang, B., Zhu, H., Cao, H., Tian, J.: Towards personalized context-aware recommendation by mining context logs through topic models. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012. LNCS (LNAI), vol. 7301, pp. 431–443. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-30217-6_36 CrossRefGoogle Scholar
  20. 20.
    Yue, K., Fang, Q., Wang, X., Li, J., Liu, W.: A parallel and incremental approach for data-intensive learning of bayesian networks. IEEE Trans. Cybern. 45(12), 2890–2904 (2015)CrossRefGoogle Scholar
  21. 21.
    Zhao, Z., Cheng, Z., Hong, L., Chi, E.H.: Improving user topic interest profiles by behavior factorization. In: WWW 2015, pp. 1406–1416. ACM (2015)Google Scholar

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

Personalised recommendations