基于深度信念网与隐变量模型的用户偏好建模

User Preference Modeling Based on Deep Belief Network and Latent Variable Model

从高维、稀疏的用户评分数据中构建用户偏好模型,存在迭代计算复杂度高、中间结果规模大和难以实现有效推理等问题。为此,提出一种基于深度信念网(DBN)和贝叶斯网(BN)的用户偏好建模方法。采用DBN对评分数据进行分类,用隐变量表示不能直接观测到的用户偏好,利用含隐变量的BN描述评分数据中蕴含的相关属性间的依赖关系及其不确定性。在MovieLens和大众点评数据集上的实验结果表明,该方法能够有效描述评分数据中与用户偏好相关的各属性间的依赖关系,其精确率和执行效率均高于隐变量模型。

To address complex iterative computations,large-scale intermediate results and ineffective inference of user preference modeling from high dimensional and sparse user rating data,this paper proposes a user preference modeling method based on Deep Belief Network(DBN)and Bayesian Network(BN).The DBN is used to classify rating data,and the latent variables are used to represent user preferences that cannot be directly observed.Then,the BN with latent variables is used to describe the uncertain dependences among related attributes in rating data.Experimental results on MovieLens and DianPing datasets show that the proposed method can effectively describe the dependences relationship between attributes related to user preferences in rating data,and its precision and execution efficiency are higher than that of Latent Variable Model(LVM).