Interactive Knowledge Graph Attention Network for Recommender Systems

Recent progress in personalized recommendation has shown great potential in exploiting structure information provided by a knowledge graph (KG). As a heterogeneous information network, KG contains rich semantic relatedness among entities, which contributes to addressing notorious issues such as data sparsity and cold start. State-of-the-art KG-based recommendation approaches try to propagate information along KG links to encode long-range connectivities into hidden representations. However, most of them only model the user or item representation independently, lacking a focus on user-item interaction. To this end, we propose the Interactive Knowledge Graph Attention Network (IKGAT), which directly models user-item interaction and high-order structure information within KG. For the user representation, following an interactive attention mechanism, we use the item to attend over the user's neighbors and then propagate their information to update the representation. Such a process is extended to multi-hops away to obtain richer neighborhood information. Similarly, the item representation is updated under the supervision of the user. With that design, IKGAT can capture collaborative signals and user preferences effectively. Experiment results on three public datasets show that IKGAT consistently outperforms the state-of-the-art approaches, especially when the dataset is sparse.