A Spatiotemporal Graph Neural Network for session-based recommendation
作者：
Highlights：
• Simulate users’ behavior patterns in the session without destroying the click order
• Highlight the critical preferences of users during the simulate process.
• Use gated fusion functions to combine global behavioral interest and current interest.
• Auto-regressive moving average convolution filter evaluates the user preferences.
• Use graph neural networks to obtain richer potential information.
摘要
•Simulate users’ behavior patterns in the session without destroying the click order•Highlight the critical preferences of users during the simulate process.•Use gated fusion functions to combine global behavioral interest and current interest.•Auto-regressive moving average convolution filter evaluates the user preferences.•Use graph neural networks to obtain richer potential information.
论文关键词：Auto-regressive moving average filter,Session-based recommendation,Spatiotemporal graph neural network,User behavior pattern
论文评审过程：Received 29 June 2021, Revised 18 October 2021, Accepted 28 March 2022, Available online 9 April 2022, Version of Record 6 May 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117114
