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Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Knowledge graph (KG) plays an increasingly important role to improve the recommendation performance and interpretability. A recent technical trend is to design end-to-end models based on the information propagation schemes. However, existing propagationbased methods fail to (1) model the underlying hierarchical structures and relations, and (2) capture the high-order collaborative signals of items for learning high-quality user and item representations. In this paper, we propose a new model,doi:10.1145/3477495.3531987 fatcat:drb4k3f3ufczdawlcys3pouiva