Multiple Behaviors Recommendation with Graph Learning

Silin Zhou, Junjie Zhang, Lisi Chen, Shuo Shang

Abstract: Recommendation systems have been extensively investigated by existing studies. However, previous work only targets a single type of user behavior (e.g., purchase) while ignoring the fact that users usually have multiple behaviors when browsing products (e.g., view, click, add-to-cart). These different behaviors can generate a large number of attributes about users. Meanwhile, in typical collaborative filtering (CF) systems, users and items are generally handled separately. Therefore, the associativity between users and items is not taken into account. To solve the above problems, we propose a novel framework named as Multiple Behaviors recommendation with Graph Learning (MBGL). To capture multiple characteristics of users and items, we construct a knowledge graph with multiple entity relations between users and items from a variety of data, such as purchase, view, and add-to-cart. We apply the knowledge graph embedding (KGE) method to learn the pre-training embedding of entity and relation. To better learn high-hop embedding from multi-behavior data, we construct a heterogeneous user-item graph and further design relation-aware GAT to learn graph embedding with relation type. To support high-order dependency in the graph, we introduce the residual network to solve feature smoothing and vanishing gradient problems. Moreover, to obtain the high-quality user and item embedding, we design an attention fusion layer to learn the fusion embedding and adopt the multi-task learning to predict users' preferences under different behaviors. Experiment results on real-world dataset show that our model outperforms other recommendation methods.