SEDGN: Sequence enhanced denoising graph neural network for session-based recommendation

作者：

Highlights：

• Propose a novel method for session-based Recommendations.

• Adopt GRU to enhance the ability of GNN to model the sequence.

• Design the modules to alleviate the impacts of natural noise in session.

• Design the strategies to get more accurate item representations for the session.

摘要

•Propose a novel method for session-based Recommendations.•Adopt GRU to enhance the ability of GNN to model the sequence.•Design the modules to alleviate the impacts of natural noise in session.•Design the strategies to get more accurate item representations for the session.

论文关键词：Session-based recommendation,Graph neural network,Denoising,Sequence enhanced

论文评审过程：Received 25 October 2021, Revised 24 April 2022, Accepted 25 April 2022, Available online 6 May 2022, Version of Record 13 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117391