Heterogeneous type-specific entity representation learning for recommendations in e-commerce network

Abstract

In heterogeneous e-commerce recommender systems, the type and attribute information of users and products contain rich semantics, which can benefit the prediction and explanation of user ratings of interesting items. Existing studies include collaborative and content-based recommendations that mainly capture semantic features by considering user–item interactions or behavioral history records, which ignores the explanatory role of the product type and attribute. In this paper, we first propose an attentional attribute and interaction method used to model the semantic embeddings of users and items. We then construct a type-specific matrix to exploit heterogeneous type-specific information to learn user and item representations. The incorporated heterogeneous type information helps capture a user’s latent features that solve the sparsity problem of user–item interactions for the recommender systems. Further, the rating relationship of the nodes is predicted through the translation mechanism based on user and items’ type-specific representations. Extensive experimental results on real-world datasets demonstrate the superior performance of the proposed model over several state-of-the-art methods and show the visual interpretability for rating behaviors in e-commerce recommender systems.