Incorporating global and local social networks for group recommendations

作者：

Highlights：

• We propose GLOW for group recommendations from global and local social networks, fully exploiting social interaction at macro and micro levels.

• Multi-layer attentive GCN based Global Network Diffusion module is proposed to model the social influence diffusion in social networks.

• We propose a multi-channel attention based LNF module to model the group decision-making process, capturing multiple types of interaction among group members and dynamically assigning different weights to each member.

• Experiments on two real-world datasets prove that our model achieves state-of-the-art in group recommendation.

摘要

•We propose GLOW for group recommendations from global and local social networks, fully exploiting social interaction at macro and micro levels.•Multi-layer attentive GCN based Global Network Diffusion module is proposed to model the social influence diffusion in social networks.•We propose a multi-channel attention based LNF module to model the group decision-making process, capturing multiple types of interaction among group members and dynamically assigning different weights to each member.•Experiments on two real-world datasets prove that our model achieves state-of-the-art in group recommendation.

论文关键词：Group recommendation,Recommendation systems,Graph neural network,Social network analysis,Graph-based method

论文评审过程：Received 30 June 2020, Revised 12 January 2022, Accepted 20 February 2022, Available online 22 February 2022, Version of Record 27 February 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108601