Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
Zihan Lin, Changxin Tian, Yupeng Hou, Wayne Xin Zhao
Abstract
Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users’ preference over items by modeling the user-item interaction graphs. Despite the effectiveness, these methods suffer from data sparsity in real scenarios. In order to reduce the influence of data sparsity, contrastive learning is adopted in graph collaborative filtering for enhancing the performance. However, these methods typically construct the contrastive pairs by random sampling, which neglect the neighboring relations among users (or items) and fail to fully exploit the potential of contrastive learning for recommendation.
Topics & Concepts
Computer scienceGraphCollaborative filteringArtificial intelligenceNatural language processingTheoretical computer scienceMachine learningRecommender systemRecommender Systems and TechniquesHuman Mobility and Location-Based AnalysisAdvanced Graph Neural Networks