DKEN: Deep knowledge-enhanced network for recommender systems

Abstract Despite that existing knowledge graphs embedding (KGE) based methods can achieve better recommendation performance compared with deep learning based ones, such improvement is limited due to lack of capturing the shared information between user-item interaction and item-item relation encoded in knowledge graph (KG) by fully leveraging the implicit and explicit relationship. To address this issue, in this paper, we propose a principled deep knowledge-enhanced network (DKEN) framework based on deep learning and KGE to model the semantics of entities and relations encoded in the KG. In particular, the DKEN utilizes deep neural networks (DNN) to learn higher-order feature interactions and ensembles KGE features with DNN features into an end-to-end learning process naturally to exploit implicit interaction and explicitt semantic features. Furthermore, a cross information sharing (CIS) layer is designed to facilitate information sharing between items and entities, and two aggregators are developed to improve the performance of the model. Extensive experiments on several public datasets, as well as online AB tests of an industrial recommendation scenario in the Ant Financial Service Group, demonstrate that DKEN achieves remarkably better performance than several state-of-the-art baselines.

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