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扮演着关键角色。深度学习技术（如RNN、GNN及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，由于现实场景中存在数据稀疏性问题，监督学习方法在有效学习表征方面面临局限。为此，自监督学习技术应运而生，其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。基于自监督学习的推荐系统借助未标注数据提取有意义表征，即使面对数据稀疏场景也能做出准确预测与推荐。本文对面向推荐系统的自监督学习框架进行了全面综述，深入分析了超过170篇相关论文。我们系统探索了九种不同场景，揭示了自监督学习增强型推荐器在不同环境中的全貌。针对每个领域，我们详细阐述了对比学习、生成学习和对抗学习等自监督学习范式，以呈现自监督学习在不同情境下增强推荐系统的技术细节。相关开源资料持续维护于 https://github.com/HKUDS/Awesome-SSLRec-Papers。