Category-aware Multi-relation Heterogeneous Graph Neural Networks for session-based recommendation

作者：

Highlights：

• We point out that there exists not only category–category relation but also item–category relation.

• A session-based recommendation (SBR) model, CM-HGNN, is proposed, which utilizes both types of relations.

• In CM-HGNN, we propose to construct an ICHG to model both types of relations.

• We propose a multi-relation heterogeneous graph convolution method which is adopted in CM-HGNN.

• Experimental results illustrate that the CM-HGNN outperforms the state-of-the-art SBR models.

摘要

•We point out that there exists not only category–category relation but also item–category relation.•A session-based recommendation (SBR) model, CM-HGNN, is proposed, which utilizes both types of relations.•In CM-HGNN, we propose to construct an ICHG to model both types of relations.•We propose a multi-relation heterogeneous graph convolution method which is adopted in CM-HGNN.•Experimental results illustrate that the CM-HGNN outperforms the state-of-the-art SBR models.

论文关键词：Session-based recommendation,Graph neural network,Category information,Heterogeneous graph

论文评审过程：Received 8 March 2022, Revised 16 May 2022, Accepted 9 June 2022, Available online 18 June 2022, Version of Record 28 June 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.109246