Balancing Self-Presentation and Self-Hiding for Exposure-Aware Recommendation Based on Graph Contrastive Learning
Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Leqi Zheng, Chaokun Wang, Ziyang Liu, Canzhi Chen, Cheng Wu, Hongwei Li
Institutions: Tsinghua University, Beijing Institute of Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412377179