Self-Supervised Learning for Recommender Systems: A Survey

TL;DR

This survey reviews self-supervised learning techniques in recommender systems, categorizing methods, analyzing their strengths and weaknesses, and providing an open-source library for empirical comparison to advance research in handling sparse data.

Contribution

It offers a comprehensive taxonomy of SSR methods, introduces an open-source benchmarking library, and provides empirical insights into effective self-supervised signals for recommendation.

Findings

Contrastive methods perform best on sparse data

Hybrid SSR approaches outperform single-category methods

Empirical results guide optimal self-supervised signal selection

Abstract

In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data. Self-supervised learning (SSL), as an emerging technique for learning from unlabeled data, has attracted considerable attention as a potential solution to this issue. This survey paper presents a systematic and timely review of research efforts on self-supervised recommendation (SSR). Specifically, we propose an exclusive definition of SSR, on top of which we develop a comprehensive taxonomy to divide existing SSR methods into four categories: contrastive, generative, predictive, and hybrid. For each category, we elucidate its concept and formulation, the involved methods, as well as its pros and cons. Furthermore, to facilitate empirical comparison, we release an open-source library SELFRec (https://github.com/Coder-Yu/SELFRec), which incorporates a wide range of SSR models and benchmark datasets. Through rigorous experiments using this library, we derive and report some significant findings regarding the selection of self-supervised signals for enhancing recommendation. Finally, we shed light on the limitations in the current research and outline the future research directions.