Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured data in recommender systems. However, real-life recommendation scenarios usually involve heterogeneous relationships (e.g., social-aware user influence, knowledge-aware item dependency) which contains fruitful information to enhance the user preference learning. In this paper, we study the problem of heterogeneous graph-enhanced relational learning for recommendation. Recently, contrastive self-supervised learning has become successful in recommendation. In light of this, we propose a Heterogeneous Graph Contrastive Learning (HGCL), which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastive learning-enhanced knowledge transfer across different views. However, the influence of heterogeneous side information on interactions may vary by users and items. To move this idea forward, we enhance our heterogeneous graph contrastive learning with meta networks to allow the personalized knowledge transformer with adaptive contrastive augmentation. The experimental results on three real-world datasets demonstrate the superiority of HGCL over state-of-the-art recommendation methods. Through ablation study, key components in HGCL method are validated to benefit the recommendation performance improvement. The source code of the model implementation is available at the link https://github.com/HKUDS/HGCL.

翻译：图神经网络（GNNs）已成为推荐系统中建模图结构数据的强大工具。然而，现实推荐场景通常涉及异构关系（如社交感知的用户影响力、知识感知的物品依赖关系），这些关系蕴含丰富信息，有助于增强用户偏好学习。本文研究异构图表征的关系学习在推荐中的应用问题。近来，对比自监督学习在推荐领域取得了成功。受此启发，我们提出异构图对比学习（HGCL）方法，该方法能够将异构关系语义融入用户-物品交互建模中，并通过对比学习增强不同视图间的知识迁移。然而，异构辅助信息对交互的影响可能因用户和物品而异。为推进这一思路，我们引入元网络对异构图对比学习进行增强，实现自适应对比增强的个性化知识迁移。在三个真实数据集上的实验结果表明，HGCL方法优于当前最先进的推荐方法。通过消融实验，验证了HGCL方法中的关键组件对提升推荐性能的有效性。模型实现的源代码可在链接https://github.com/HKUDS/HGCL获取。