【摘要】 用户偏好是决定用户对商品评分的隐含变量,以构建包含用户偏好的隐变量模型、描述评分数据中相关属性间任意形式依赖关系及其不确定性为主要目标,以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,由商品评分数据构建不含隐变量的商品评分模型,提出基于半团结构向其中插入描述用户偏好的隐变量的方法,从而构建包含用户偏好的隐变量模型,并给出基于EM算法的隐变量模型参数估计方法,进而提出隐变量模型的概率推理算法和相应的商品评分预测方法.建立在MovieLens和Book-Crossing数据上的实验结果表明,本文提出的隐变量模型构建和相应的评分预测方法是有效的.更多还原

【Abstract】 User preference is a latent variable that determines online product ratings. This paper is to construct the latent variable model with user preference,and describe arbitrary dependence relationships as well as the corresponding uncertainties in rating data by adopting Bayesian network as the preliminary framework. In this paper,we start from the rating data and construct the product rating model without latent variables at first. Then,we give the method for inserting latent variables based the semi-clique structure,so the model can be constructed to describe user preference by the inserted a latent variable. Following,we give the EM-algorithm based method for estimating parameters in the latent variable model. Finally,we propose the algorithm for probabilistic inferences of the latent variable model and the method for predicting user ratings. Experimental results on the MovieLens and Book-Crossing datasets show that our method is effective.更多还原