Neural architecture-based recommender systems have achieved tremendous
success in recent years. However, when dealing with highly sparse data, they
still fall short of expectation. Self-supervised learning (SSL), as an emerging
technique to learn with unlabeled data, recently has drawn considerable
attention in many fields. There is also a growing body of research proceeding
towards applying SSL to recommendation for mitigating the data sparsity issue.
In this survey, a timely and systematical review of the research efforts on
self-supervised recommendation (SSR) is presented. Specifically, we propose an
exclusive definition of SSR, on top of which we build a comprehensive taxonomy
to divide existing SSR methods into four categories: contrastive, generative,
predictive, and hybrid. For each category, the narrative unfolds along its
concept and formulation, the involved methods, and its pros and cons.
Meanwhile, to facilitate the development and evaluation of SSR models, we
release an open-source library SELFRec, which incorporates multiple benchmark
datasets and evaluation metrics, and has implemented a number of
state-of-the-art SSR models for empirical comparison. Finally, we shed light on
the limitations in the current research and outline the future research
directions.

本文针对神经架构型推荐系统在高度稀疏数据上存在的问题，提出了自监督学习作为缓解数据稀疏性的新技术，并介绍了一种基于分类的综述方法，开发了一个 SELFRec 开源库，其中包含多个基准数据集和评估指标，以及实施了一些先进的 SSR 模型进行实证比较。同时讨论了当前研究中的限制和未来研究方向。