Deep User Representation Construction Model for Collaborative Filtering

Daomin Ji, Zhenglong Xiang, Yuanxiang Li

Abstract: Model-based collaborative filtering (CF) methods can be divided into user-item methods and item-item methods. In most cases, both of them can be seen as modeling the user-item interaction and the only difference between them is that they adopt different ways to build user representations. User-item methods obtain user representations by directly assigning each user a real-valued vector and do not consider users’ historical item information. However, users’ historical item information can reflect users’ preferences to some extent and can alleviate the problem of data sparsity. Ignoring this information may lead to incomplete construction of user representations and vulnerability to data sparsity. Although existing item-item methods address this problem by using the users’ historical items to build the user representations, they always use the same vector to represent the same historical item for different users, which may limit the expressiveness and further improvement of the models. In this paper, we propose Deep User Representation Construction Model (DURCM) to construct user presentations in a more effective and robust way. Specially, different from existing item-item methods that directly use historical item vectors to build user representations, we first adopt a conversion module to convert a user’s historical item vectors into personalized item vectors, which enables that even the same item has different expressions for different users. Second, we design a special attention module to automatically assign weights to these personalized item vectors when constructing the users’ final representations. We conduct comprehensive experiments on four real-world datasets and the results verify the effectiveness of our proposed methods.