Mixed Supervised Graph Contrastive Learning for Recommendation

Understand

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.