Graph-based collaborative filtering recommendations can more effectively explore the interaction information between users and items. However, its performance is still affected by the problems of data sparsity and low-quality representation learning. To address this, we propose a recommendation model named Multi-behavior-based Graph Contrastive Learning (MBGCL for short) Recommendation. Firstly, we leverage a graph convolutional network that can balance recommendation accuracy and novelty to learn multi-behavior data. We apply advanced MLP modules to enhance the nonlinearity of the representations obtained from graph convolutional network and integrate the learned multi-behavior representations. Secondly, we enhance representation capability and alleviate popularity bias through two contrastive learning auxiliary tasks. The multi-behavior contrastive learning task contrastively learns the target behavior and other auxiliary behavior subgraphs. The embedding-noise contrastive learning task aims to introduce noise into different behavior representations and generate augmented views for contrastive learning. Finally, we directly optimize the learning objectives by jointly training the graph collaborative filtering recommendation task with the contrastive learning auxiliary tasks. The empirical results on two real-world datasets validate the effectiveness of our model. Our model outperforms the SOTA baselines in terms of recommendation accuracy and novelty metrics.

基于图的协同过滤推荐可以更有效地挖掘用户和物品之间的交互信息。然而，其性能仍然受到数据稀疏和低质量表示学习问题的影响。为了解决这个问题，我们提出了一种名为基于多行为的图对比学习（简称MBGCL）推荐的推荐模型。首先，我们利用可以平衡推荐准确性和新颖性的图卷积网络来学习多行为数据。我们应用先进的 MLP 模块来增强从图卷积网络获得的表示的非线性，并整合学习到的多行为表示。其次，我们通过两个对比学习辅助任务来增强表示能力并减轻流行偏差。多行为对比学习任务对比学习目标行为和其他辅助行为子图。嵌入噪声对比学习任务旨在将噪声引入不同的行为表示中，并生成用于对比学习的增强视图。最后，我们通过联合训练图协同过滤推荐任务和对比学习辅助任务来直接优化学习目标。两个现实世界数据集的实证结果验证了我们模型的有效性。我们的模型在推荐准确性和新颖性指标方面优于 SOTA 基线。