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:基于深度学习的推荐系统近年来取得了显著的成功。然而,这些方法通常严重依赖于标记数据(即用户-物品交互),存在数据稀疏和冷启动等问题。自监督学习是一种新兴的范式,它从无标记数据中提取信息,为解决这些问题提供了思路。特别是,对比自监督学习由于其灵活性和有前途的性能而吸引了广泛的关注,并成为自监督学习推荐方法的主要分支。在本文中,我们提供了当前对比自监督学习推荐方法的最新全面评述。首先,我们提出了这些方法的统一框架。然后,我们根据框架的关键组件,包括视图生成策略、对比任务和对比目标,提出了一种分类方法。对于每个组件,我们提供了详细的描述和讨论,以指导选择适当的方法。最后,我们概述了未来研究的开放问题和有前途的方向。