Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks

作者：

Highlights：

• A new framework (COAT) is proposed for personalized knowledge-aware recommendation.

• Collaborative and attentive GNNs are designed to jointly model the UI and KG graphs.

• Novel attention mechanisms are designed to achieve personalization.

• An efficient graph convolutional layer is employed to tackle the sparsity issue.

• COAT outperforms 10 state-of-the-art recommendation methods on benchmark datasets.

摘要

•A new framework (COAT) is proposed for personalized knowledge-aware recommendation.•Collaborative and attentive GNNs are designed to jointly model the UI and KG graphs.•Novel attention mechanisms are designed to achieve personalization.•An efficient graph convolutional layer is employed to tackle the sparsity issue.•COAT outperforms 10 state-of-the-art recommendation methods on benchmark datasets.

论文关键词：Recommender system,Graph convolutional network,Attention mechanism,Knowledge graph

论文评审过程：Received 18 October 2020, Revised 27 November 2021, Accepted 5 March 2022, Available online 6 March 2022, Version of Record 27 March 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108628