Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences. Deep learning techniques, such as RNNs, GNNs, and Transformer architectures, have significantly propelled the advancement of recommender systems by enhancing their comprehension of user behaviors and preferences. However, supervised learning methods encounter challenges in real-life scenarios due to data sparsity, resulting in limitations in their ability to learn representations effectively. To address this, self-supervised learning (SSL) techniques have emerged as a solution, leveraging inherent data structures to generate supervision signals without relying solely on labeled data. By leveraging unlabeled data and extracting meaningful representations, recommender systems utilizing SSL can make accurate predictions and recommendations even when confronted with data sparsity. In this paper, we provide a comprehensive review of self-supervised learning frameworks designed for recommender systems, encompassing a thorough analysis of over 170 papers. We conduct an exploration of nine distinct scenarios, enabling a comprehensive understanding of SSL-enhanced recommenders in different contexts. For each domain, we elaborate on different self-supervised learning paradigms, namely contrastive learning, generative learning, and adversarial learning, so as to present technical details of how SSL enhances recommender systems in various contexts. We consistently maintain the related open-source materials at https://github.com/HKUDS/Awesome-SSLRec-Papers.

翻译：推荐系统通过基于用户个性化偏好提供精准推荐，在应对信息过载挑战中发挥着关键作用。深度学习技术（如RNN、GNN与Transformer架构）显著推动了推荐系统的发展，增强了其对用户行为与偏好的理解能力。然而，监督学习方法在实际场景中面临数据稀疏性问题，导致其在表征学习方面的能力受限。为此，自监督学习（SSL）技术应运而生，通过利用数据内在结构生成监督信号，无需完全依赖标注数据。借助未标注数据并提取有意义的表征，采用SSL的推荐系统即使在数据稀疏条件下仍能实现精准预测与推荐。本文对面向推荐系统的自监督学习框架进行了全面综述，系统分析了超过170篇相关论文。我们探索了九种不同场景，深入理解SSL增强型推荐系统在不同情境下的表现。针对每种领域，我们详细阐述了对比学习、生成学习与对抗学习三种自监督学习范式，揭示SSL在不同场景中增强推荐系统的技术细节。我们持续维护相关开源资料，发布在https://github.com/HKUDS/Awesome-SSLRec-Papers。