Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation

Leqi Zheng (Tsinghua University) · Chaokun Wang (Tsinghua University) · Zixin Song (Tsinghua University) · Cheng Wu (Tsinghua University, Tsinghua University) · Shannan Yan (Tsinghua University) · Jiajun Zhang (Institute of automation, Chinese academy of science, Chinese Academy of Sciences) · Ziyang Liu (Tsinghua University)

adaptive prediction strategycomputational inefficiencycross-channel distribution calibrationdual-channel feedback fusiondual-channel graph embeddinggraph contrastive learning information exchange model-agnosticnegative feedbackpublic datasetsrecommendation performancerecommender systemssigned dual-channel graph contrastive learningstructural consistencytwo-stage optimization strategy

Traditional recommender systems have relied heavily on positive feedback for learning user preferences, while the abundance of negative feedback in real-world scenarios remains underutilized. To address this limitation, recent years have witnessed increasing attention on leveraging negative feedback in recommender systems to enhance recommendation performance. However, existing methods face three major challenges: limited model compatibility, ineffective information exchange, and computational inefficiency. To overcome these challenges, we propose a model-agnostic Signed Dual-Channel Graph Contrastive Learning (SDCGCL) framework that can be seamlessly integrated with existing graph contrastive learning methods. The framework features three key components: (1) a Dual-Channel Graph Embedding that separately processes positive and negative graphs, (2) a Cross-Channel Distribution Calibration mechanism to maintain structural consistency, and (3) an Adaptive Prediction Strategy that effectively combines signals from both channels. Building upon this framework, we further propose a Dual-channel Feedback Fusion (DualFuse) model and develop a two-stage optimization strategy to ensure efficient training. Extensive experiments on four public datasets demonstrate that our approach consistently outperforms state-of-the-art baselines by substantial margins while exhibiting minimal computational complexity.