Personalize d knowle dge-aware recommendation with collaborative and attentive graph convolutional networks

Knowledge graphs (KGs) are increasingly used to solve the data sparsity and cold start problems of col-laborative filtering. Recently, graph neural networks (GNNs) have been applied to build KG-based rec-ommender systems and achieved competitive performance. However, existing GNN-based methods are either limited in their ability to capture fine-grained semantics in a KG, or insufficient in effectively mod-eling user-item interactions. To address these issues, we propose a novel framework with collaborative and attentive graph convolutional networks for personalized knowledge-aware recommendation. Partic-ularly, we model the user-item graph and the KG separately and simultaneously with an efficient graph convolutional network and a personalized knowledge graph attention network, where the former aims to extract informative collaborative signals, while the latter is designed to capture fine-grained semantics. Collectively, they are able to learn meaningful node representations for predicting user-item interactions. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method compared with state-of-the-arts.(c) 2022 Elsevier Ltd. All rights reserved.