Mixed Supervised Graph Contrastive Learning for Recommendation
arxiv(2024)
摘要
Recommender systems (RecSys) play a vital role in online platforms, offering
users personalized suggestions amidst vast information. Graph contrastive
learning aims to learn from high-order collaborative filtering signals with
unsupervised augmentation on the user-item bipartite graph, which predominantly
relies on the multi-task learning framework involving both the pair-wise
recommendation loss and the contrastive loss. This decoupled design can cause
inconsistent optimization direction from different losses, which leads to
longer convergence time and even sub-optimal performance. Besides, the
self-supervised contrastive loss falls short in alleviating the data sparsity
issue in RecSys as it learns to differentiate users/items from different views
without providing extra supervised collaborative filtering signals during
augmentations. In this paper, we propose Mixed Supervised Graph Contrastive
Learning for Recommendation (MixSGCL) to address these concerns. MixSGCL
originally integrates the training of recommendation and unsupervised
contrastive losses into a supervised contrastive learning loss to align the two
tasks within one optimization direction. To cope with the data sparsity issue,
instead unsupervised augmentation, we further propose node-wise and edge-wise
mixup to mine more direct supervised collaborative filtering signals based on
existing user-item interactions. Extensive experiments on three real-world
datasets demonstrate that MixSGCL surpasses state-of-the-art methods, achieving
top performance on both accuracy and efficiency. It validates the effectiveness
of MixSGCL with our coupled design on supervised graph contrastive learning.
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