Research map / Graph learning / Graph self-supervised learning
Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
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Proposes a signed dual-channel graph contrastive learning framework that exploits negative feedback alongside positive feedback for recommendation.
Problem
Traditional recommender systems rely mainly on positive feedback, leaving abundant negative feedback underutilized; existing negative-feedback methods face several challenges (the abstract is truncated).
Approach
A signed dual-channel graph contrastive learning design that models positive and negative feedback in separate channels within a graph, using contrastive learning to leverage negative signals for user preference learning.
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