Balancing Self-Presentation and Self-Hiding for Exposure-Aware Recommendation Based on Graph Contrastive Learning

Recent advances in graph contrastive learning (GCL) have significantly enhanced recommendation systems. However, most existing approaches predominantly focus on optimizing training data fit while overlooking exposure bias, a critical issue that can substantially impact recommendation effectiveness. Drawing inspiration from sociological theories of human interaction patterns-specifically how individuals balance self-presentation and self-hiding behaviors in social contexts-this paper proposes BPH4Rec, a novel Balancing self-Presentation and self-Hiding approach for exposure-aware Recommendation based on GCL. Within the GCL framework, BPH4Rec introduces two complementary mechanisms: (1) a self-hiding mechanism that modifies the adjacency matrix of contrastive views through custom inverse propensity scoring (IPS), effectively addressing exposure bias, and (2) a self-presentation mechanism that incorporates densification factors during matrix reconstruction to mitigate sparsity-induced biases. Through extensive evaluation on six public benchmark datasets, BPH4Rec demonstrates substantial improvements over state-of-the-art baselines, particularly in promoting long-tail item discovery while maintaining recommendation accuracy.