中文摘要：

随着web2．0的不断普及和电子商务应用的迅速发展，大规模的在线评价数据不断产生，使用户行为数据分析和用户行为建模成为可能，具有重要意义．考虑到用户评价数据和评价行为的动态性，提出以带有隐变量的贝叶斯网作为各属性间依赖关系及其不确定性表示的基本框架，构建既能刻画用户评价数据中各属性间相互依赖的不确定性、也能描述用户行为动态性的评价行为模型．首先，以贝叶斯信息标准（BIC）分值作为模型与数据拟合度的度量标准，提出基于打分搜索方法来构建各时间片的隐变量模型，并给出基于期望最大（EM）算法的隐变量取值填充方法；其次，基于条件互信息和时序的不可逆性，提出了相邻时间片间隐变量模型的构建方法．建立在MovieLens数据集上的实验结果验证了提出的动态用户行为建模方法的高效性及有效性．

英文摘要：

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.