Graph Augmentation Empowered Contrastive Learning for Recommendation
Lixiang Xu,
Yusheng Liu,
Tong Xu
et al.
Abstract:The application of contrastive learning (CL) to collaborative filtering (CF) in recommender systems has achieved remarkable success. CL-based recommendation models mainly focus on creating multiple augmented views by employing different graph augmentation methods and utilizing these views for self-supervised learning. However, current CL methods for recommender systems usually struggle to fully address the problem of noisy data. To address this problem, we propose the
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raph
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