Mixed Supervised Graph Contrastive Learning for Recommendation

TL;DR

This paper introduces MixSGCL, a supervised contrastive learning approach for recommendation systems that integrates recommendation and contrastive tasks into a unified framework, improving performance and addressing data sparsity.

Contribution

MixSGCL combines recommendation and contrastive learning into a supervised framework and proposes node-wise and edge-wise mixup to enhance data utilization.

Findings

Outperforms state-of-the-art methods on three real-world datasets.

Achieves better accuracy and efficiency in recommendation tasks.

Effectively alleviates data sparsity issues in RecSys.

Abstract

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.