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，这些方法通常高度依赖标注数据（即用户-项目交互），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注数据中提取信息的新兴范式，为解决这些问题提供了思路。其中，对比自监督学习因其灵活性和优越性能而引起广泛关注，并逐渐成为基于自监督学习的推荐方法中的主流分支。本综述对当前基于对比自监督学习的推荐方法进行了最新且全面的梳理。首先，我们提出了一个统一框架来整合这些方法。随后，基于框架的关键组成部分（包括视图生成策略、对比任务和对比目标）构建了分类体系。针对每个组成部分，我们提供了详细描述和讨论，以指导选择合适的方法。最后，我们总结了现有问题并展望了未来研究方向。