  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.
