Analyzing Rating Data and Modeling Dynamic Behaviors of Users Based on the Bayesian Network

被引：0
作者：
Wang F. [1 ]
Yue K. [1 ]
Sun Z. [2 ]
Wu H. [1 ]
Feng H. [1 ]
机构：
[1] School of Information Science and Engineering, Yunnan University, Kunming
[2] Department of Science and Technology, Yunnan University, Kunming
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2017年 / 54卷 / 07期
关键词：
Bayesian network; Dynamic behavior model; Latent variable model; Time-series; User rating data;
D O I：
10.7544/issn1000-1239.2017.20160556
中图分类号：
学科分类号：
摘要：
With the rapid development of Web2.0 and the e-commerce applications, large-scale online rating data are generated, which makes it possible to analyze users behavior data and model user behaviors. Considering the dynamic property of rating data and user behaviors, in this paper we adopt the Bayesian network with a latent variable (abbreviated as latent variable model) as the framework for describing mutual dependencies and corresponding uncertainties, and then construct the model that can reflect not only the uncertainty of dependence relationships among attributes in rating data but also the dynamic property of user behaviors. We first adopt the Bayesian information criterion (BIC) as the coincidence measure between candidate model and rating data, and then propose the scoring-and-search based method to construct the latent variable model. Then, we give the method for filling latent variable values based on the expectation maximization (EM) algorithm. Further, we propose the method for constructing the latent variable model between adjacent time slices based on conditional mutual information and irreversibility of time series. Finally, experimental results established on the MovieLens data set verify the efficiency and effectiveness of the method proposed in this paper. © 2017, Science Press. All right reserved.
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页码：1488 / 1499
页数：11
相关论文
共 21 条
  • [11] Kapoor K., Subbian K., Srivastava J., Et al., Just in time recommendations: Modeling the dynamics of boredom in activity streams, Proc of the 8th ACM Int Conf on Web Search and Data Mining, pp. 233-242, (2015)
  • [12] Wang Z., Li Q., Wang J., Et al., Real time personalized recommendation based on implicit user feedback data stream, Chinese Journal of Computers, 39, 1, pp. 52-64, (2016)
  • [13] Yue K., Fang Q., Wang X., Et al., A parallel and incremental approach for data intensive learning of Bayesian networks, IEEE Trans on Cybernetics, 45, 12, (2015)
  • [14] Dempster A.P., Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, 39, 1, pp. 1-38, (1977)
  • [15] Wang S., Yuan S., Research on learning Bayesian networks structure with missing data, Journal of Software, 15, 7, pp. 1042-1048, (2004)
  • [16] Liu W., Yue K., Liu S., Et al., Qualitative probabilistic network based modeling of temporal causalities and its application to feedback loop identification, Information Sciences, 178, 7, pp. 1803-1824, (2008)
  • [17] MovieLens data sets
  • [18] Harvey M., Carman M.J., Ruthven I., Et al., Bayesian latent variable models for collaborative item rating prediction, Proc of the 20th ACM Conf on Information and Knowledge Management, pp. 699-708, (2011)
  • [19] Zhao G., Qian X., Service objective evaluation via exploring social users' rating behaviors, Proc of 2015 IEEE Int Conf on Multimedia Big Data, pp. 228-235, (2015)
  • [20] Sun G., Wu L., Liu Q., Et al., Recommendations based on collaborative filtering by exploiting sequential behaviors, Journal of Software, 24, 11, pp. 2721-2733, (2013)
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