# Socially-Aware Self-Supervised Tri-Training for Recommendation

arXiv: https://arxiv.org/abs/2106.03569 · HTML: https://mlsanity.com/paper/2106.03569/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2021.

## Built on by (most cited first, 30 of 32)

- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [A Survey of Graph Neural Networks for Social Recommender Systems](https://mlsanity.com/paper/2212.04481/) (2022)
- [Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation](https://mlsanity.com/paper/2109.04200/) (2021)
- [Disentangled Contrastive Learning for Social Recommendation](https://mlsanity.com/paper/2208.08723/) (2022)
- [Score-based Generative Diffusion Models for Social Recommendations](https://mlsanity.com/paper/2412.15579/) (2024)
- [Efficient Bi-Level Optimization for Recommendation Denoising](https://mlsanity.com/paper/2210.10321/) (2022)
- [SSLRec: A Self-Supervised Learning Framework for Recommendation](https://mlsanity.com/paper/2308.05697/) (2023)
- [A Review of Pseudo-Labeling for Computer Vision](https://mlsanity.com/paper/2408.07221/) (2024)
- [Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures](https://mlsanity.com/paper/2404.14942/) (2024)
- [Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks](https://mlsanity.com/paper/2311.18244/) (2023)
- [APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation](https://mlsanity.com/paper/2311.02816/) (2023)
- [Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks](https://mlsanity.com/paper/2302.14061/) (2023)
- [RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation](https://mlsanity.com/paper/2407.20684/) (2024)
- [Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2304.12228/) (2023)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation](https://mlsanity.com/paper/2112.02275/) (2021)
- [Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering](https://mlsanity.com/paper/2202.13140/) (2022)
- [STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting](https://mlsanity.com/paper/2307.02507/) (2023)
- [SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction](https://mlsanity.com/paper/2510.00080/) (2025)
- [SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation](https://mlsanity.com/paper/2405.00287/) (2024)
- [Self Contrastive Learning for Session-based Recommendation](https://mlsanity.com/paper/2306.01266/) (2023)
- [Graph Prompting for Graph Learning Models: Recent Advances and Future Directions](https://mlsanity.com/paper/2506.08326/) (2025)
- [Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation](https://mlsanity.com/paper/2304.07506/) (2023)
- [Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems](https://mlsanity.com/paper/2312.17443/) (2023)
- [IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation](https://mlsanity.com/paper/2308.15926/) (2023)
- [Learning Social Graph for Inactive User Recommendation](https://mlsanity.com/paper/2405.05288/) (2024)
- [Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2311.02938/) (2023)
- [A Social-aware Gaussian Pre-trained Model for Effective Cold-start Recommendation](https://mlsanity.com/paper/2311.15790/) (2023)
- [Prototypical Contrastive Learning through Alignment and Uniformity for Recommendation](https://mlsanity.com/paper/2402.02079/) (2024)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)

## Abstract (arXiv)

Self-supervised learning (SSL), which can automatically generate ground-truth samples from raw data, holds vast potential to improve recommender systems. Most existing SSL-based methods perturb the raw data graph with uniform node/edge dropout to generate new data views and then conduct the self-discrimination based contrastive learning over different views to learn generalizable representations. Under this scheme, only a bijective mapping is built between nodes in two different views, which means that the self-supervision signals from other nodes are being neglected. Due to the widely observed homophily in recommender systems, we argue that the supervisory signals from other nodes are also highly likely to benefit the representation learning for recommendation. To capture these signals, a general socially-aware SSL framework that integrates tri-training is proposed in this paper. Technically, our framework first augments the user data views with the user social information. And then under the regime of tri-training for multi-view encoding, the framework builds three graph encoders (one for recommendation) upon the augmented views and iteratively improves each encoder with self-supervision signals from other users, generated by the other two encoders. Since the tri-training operates on the augmented views of the same data sources for self-supervision signals, we name it self-supervised tri-training. Extensive experiments on multiple real-world datasets consistently validate the effectiveness of the self-supervised tri-training framework for improving recommendation. The code is released at https://github.com/Coder-Yu/QRec.