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
this https URL

推荐系统在面对信息过载的挑战中发挥着关键作用，通过基于个人用户偏好的个性化推荐提供了解决方案。深度学习技术（如 RNN、GNN 和 Transformer 架构）显著推动了推荐系统的发展，增强其对用户行为和偏好的理解。然而，在现实场景中，监督学习方法由于数据稀疏性而面临挑战，限制了它们有效学习表示的能力。自监督学习技术作为一种解决方案应运而生，利用固有数据结构生成监督信号，不仅依赖于已标记的数据。通过利用未标记数据和提取有意义的表示方法，利用自监督学习的推荐系统可以在面对数据稀疏性时进行准确的预测和推荐。本文对为推荐系统设计的自监督学习框架进行了全面的回顾，包括对超过 170 篇论文的深入分析。我们探索了九种不同的场景，以全面理解不同情境中使用自监督学习增强的推荐系统。对于每个领域，我们详细阐述了不同的自监督学习范式，即对比学习、生成学习和对抗学习，以展示自监督学习如何在各种上下文中增强推荐系统的技术细节。我们在此 https URL 上持续更新相关的开源材料。