scholar.google.com › citations
Abstract: Social recommendations play a crucial role in providing personalized services to users by leveraging social relationships and user sessions.
An Evolving Graph Contrastive Learning for Socially-aware Recommendation (EGCLSR) model is proposed for capturing users' fresh interests.
An Evolving Graph Contrastive Learning for Socially-aware. Recommendation (EGCLSR) model is proposed to effectively enhance the similarity between users and ...
Evolving Graph Contrastive Learning for Socially-aware Recommendation
www.semanticscholar.org › paper
An Evolving Graph Contrastive Learning for Socially-aware Recommendation (EGCLSR) model is proposed for capturing users' fresh interests and consistently ...
It enables learning from long temporal sequences on graphs and improves long-term traffic forecasting accuracy. Our model can achieve up to 35 % better long- ...
Finally, we employ graph contrastive learning to maximize the consistency of node representation across different augmented views, and further focus on domain- ...
Mar 10, 2022 · In this work, we propose a contrastive graph learning (CGL) model, which combines social information and contrastive learning in a simple and powerful way.
Missing: Evolving | Show results with:Evolving
Sep 30, 2024 · We propose a novel Multi-view Contrastive learning framework for Social Recommendation, named MultiCSR. This framework adaptively incorporates user social ...
Capturing users' fresh interests via evolving session-based social recommendation ... Evolving Graph Contrastive Learning for Socially-aware Recommendation. H ...
We propose a novel method, Candidate-aware Graph Contrastive Learning for Recommendation, called CGCL. In CGCL, we explore the relationship between the user ...