Relation-Aware Graph Attention Network for Multi-Behavior Recommendation

In practical recommendation scenarios, the types of user behaviors are usually diverse (e.g., click, add-to-cart, purchase), and different types of user behaviors can provide different aspects of user preference information. However, most existing methods only consider a single type of user behavior for modeling, which is not sufficient to fully learn complex user preferences. Besides multiple behavior types, the heterogeneous preference strength of users for items under the same behavior is also a factor that is often overlooked by most methods. Moreover, different types of behaviors may be correlated due to various factors, inadequate exploration of the implicit relationships between different types of behaviors may lead to the loss of potential information across behaviors. To solve the above problems, we propose a novel multi-behavior model with relation-aware graph attention network (RGAN), which is built on a graph-based neural architecture to explore high-order user-item relations. Specifically, we design a relation-aware attention propagation layer and an inter-behavior dependency encoder to capture heterogeneous collaborative signals from type-specific and inter-type behavior relations, respectively. During behavior integration, our proposed model automatically learns which types of behaviors are more important for assisting target behavior prediction. Extensive experiments conducted on three real-world datasets demonstrate that the RGAN model consistently outper-forms many state-of-the-art baselines, in terms of HR@n and NDCG@n.