Self-Supervised Graph Attention Collaborative Filtering for Recommendation

Self-Supervised Graph Attention Collaborative Filtering for Recommendation

Due to the complementary nature of graph neural networks and structured data in recommendations, recommendation systems using graph neural network techniques have become mainstream. However, there are still problems, such as sparse supervised signals and interaction noise, in the recommendation task...

Full description

Bibliographic Details
Main Authors: Jiangqiang Zhu, Kai Li, Jinjia Peng, Jing Qi
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Electronics
Subjects:
recommendation system
collaborative filtering
graph neural networks
self-supervised learning
multi-task learning
Online Access:https://www.mdpi.com/2079-9292/12/4/793

Internet

https://www.mdpi.com/2079-9292/12/4/793

Similar Items