Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning 论文

详细信息

发表期刊/会议

Proceedings of the ACM Web Conference 2022

发表日期

2022-04-25

发表年份

2022

摘要

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.