Deep learning-based recommender systems have achieved remarkable success in recent years. However, these methods usually heavily rely on labeled data (i.e., user-item interactions), suffering from problems such as data sparsity and cold-start. Self-supervised learning, an emerging paradigm that extracts information from unlabeled data, provides insights into addressing these problems. Specifically, contrastive self-supervised learning, due to its flexibility and promising performance, has attracted considerable interest and recently become a dominant branch in self-supervised learning-based recommendation methods. In this survey, we provide an up-to-date and comprehensive review of current contrastive self-supervised learning-based recommendation methods. Firstly, we propose a unified framework for these methods. We then introduce a taxonomy based on the key components of the framework, including view generation strategy, contrastive task, and contrastive objective. For each component, we provide detailed descriptions and discussions to guide the choice of the appropriate method. Finally, we outline open issues and promising directions for future research.

翻译：近年来，基于深度学习的推荐系统取得了显著成功。然而，这些方法通常严重依赖有标签数据（即用户-物品交互），面临数据稀疏性和冷启动等问题。自监督学习作为一种从无标签数据中提取信息的新兴范式，为解决这些问题提供了新思路。其中，对比自监督学习因其灵活性和优异的性能引起了广泛关注，并已成为基于自监督学习的推荐方法中的主导分支。本综述对当前基于对比自监督学习的推荐方法进行了全面且最新的梳理。首先，我们提出了一个统一的方法框架。随后，基于该框架的关键组件（包括视图生成策略、对比任务和对比目标）构建了分类体系。针对每个组件，我们提供了详细阐述和探讨，以指导选择合适的方法。最后，我们指出了开放性问题及未来研究的可行方向。