ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 基于贝叶斯网的评价数据分析和动态行为建模

1. 1(云南大学信息学院 昆明 650504);2(云南大学科技处 昆明 650504) (wangfei_989@163.com)
• 出版日期: 2017-07-01
• 基金资助:
国家自然科学基金项目(61472345，61562090)；云南省应用基础研究计划重点项目(2014FA023)；云南大学青年英才培育计划项目(WX173602)；云南大学创新团队培育计划项目(XT412011)；云南省教育厅科研基金项目(2016ZZX006)

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

Wang Fei1, Yue Kun1, Sun Zhengbao2, Wu Hao1, Feng Hui1

1. 1(School of Information Science and Engineering, Yunnan University, Kunming 650504);2(Department of Science and Technology, Yunnan University, Kunming 650504)
• Online: 2017-07-01

Abstract: 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.