Self-Supervised Learning for Recommender Systems: A Survey

By Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, Zi-Liang Huang. IEEE Transactions on Knowledge and Data Engineering, 2022

438 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/2e6654520d8831f1721d4ec2dd1089b5d27f460f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hongzhi Yin
2. Tong Chen
3. Wayne Xin Zhao
4. Xin Xia
5. Junliang Yu
6. Ji-rong Wen
7. Yupeng Hou
8. Jing Zhang

In Information Systems

1. Hongzhi Yin
2. Tong Chen
3. Xin Xia
4. Wayne Xin Zhao
5. Junliang Yu
6. Yupeng Hou
7. Ji-rong Wen
8. Chao Huang

Citations in the corpus

• Representation Learning with Large Language Models for Recommendation (2023, methodology): “Specifically, we utilize LightGCN [11] as the underlying backbone recommender model, which generate a ranking list of 50 candidate items preferred by each user based on learned user-item interaction probabilities.”
• Towards Universal Sequence Representation Learning for Recommender Systems (2022, methodology): “Besides directly mining sequential patterns of IDs, some works are proposed to model sequences with rich features [32, 36] or additional self-supervised signals [29, 30, 36].”
• A Survey on Self-Supervised Learning: Algorithms, Applications, and Future Trends (2023, background): “Differences from previous work : Previous works have provided reviews on SSL that cater to specific applications such as recommender systems [26], graphs [27], sequential transfer learning [28], videos [29], and adversarial pre-training of self-supervised deep networks [30].”
• Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation (2021, background): “As CL works in a self-supervised manner [41], it is inherently a possible solution to the data sparsity issue [36, 37] in recommender systems.”
• XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation (2022, methodology): “As CL usually works in a self-supervised manner [3], it is inherently a sliver bullet to the data sparsity issue [58] in recommender systems.”