# Self-Supervised Graph Co-Training for Session-based Recommendation

arXiv: https://arxiv.org/abs/2108.10560 · HTML: https://mlsanity.com/paper/2108.10560/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2021.

## Built on by (most cited first, 30 of 43)

- [Towards Graph Contrastive Learning: A Survey and Beyond](https://mlsanity.com/paper/2405.11868/) (2024)
- [Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck](https://mlsanity.com/paper/2408.00295/) (2024)
- [Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation](https://mlsanity.com/paper/2305.05848/) (2023)
- [SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation](https://mlsanity.com/paper/2405.20878/) (2024)
- [Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation](https://mlsanity.com/paper/2205.04181/) (2022)
- [Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks](https://mlsanity.com/paper/2311.18244/) (2023)
- [Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering](https://mlsanity.com/paper/2402.11523/) (2024)
- [M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations](https://mlsanity.com/paper/2209.11824/) (2022)
- [Imbalanced Node Classification Beyond Homophilic Assumption](https://mlsanity.com/paper/2304.14635/) (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)
- [Implicit Session Contexts for Next-Item Recommendations](https://mlsanity.com/paper/2208.09076/) (2022)
- [Graph Augmentation for Recommendation](https://mlsanity.com/paper/2403.16656/) (2024)
- [Self Contrastive Learning for Session-based Recommendation](https://mlsanity.com/paper/2306.01266/) (2023)
- [Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems](https://mlsanity.com/paper/2210.12940/) (2022)
- [Hypergraph Node Representation Learning with One-Stage Message Passing](https://mlsanity.com/paper/2312.00336/) (2023)
- [FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning](https://mlsanity.com/paper/2402.03481/) (2024)
- [Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation](https://mlsanity.com/paper/2404.07219/) (2024)
- [Graph Spring Network and Informative Anchor Selection for Session-based Recommendation](https://mlsanity.com/paper/2202.09502/) (2022)
- [Hierarchical Multi-Task Learning Framework for Session-based Recommendations](https://mlsanity.com/paper/2309.06533/) (2023)
- [Performance Comparison of Session-based Recommendation Algorithms based on GNNs](https://mlsanity.com/paper/2312.16695/) (2023)
- [Multi-Graph Co-Training for Capturing User Intent in Session-based Recommendation](https://mlsanity.com/paper/2412.11105/) (2024)
- [Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation](https://mlsanity.com/paper/2507.04623/) (2025)
- [Accurate Portraits of Scientific Resources and Knowledge Service Components](https://mlsanity.com/paper/2204.04883/) (2022)
- [Research on Cross-media Science and Technology Information Data Retrieval](https://mlsanity.com/paper/2204.04887/) (2022)
- [Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences](https://mlsanity.com/paper/2204.04888/) (2022)
- [Retrieval of Scientific and Technological Resources for Experts and Scholars](https://mlsanity.com/paper/2204.06142/) (2022)
- [Research on Intellectual Property Resource Profile and Evolution Law](https://mlsanity.com/paper/2204.06221/) (2022)
- [Research on Domain Information Mining and Theme Evolution of Scientific Papers](https://mlsanity.com/paper/2204.08476/) (2022)
- [Profiling and Evolution of Intellectual Property](https://mlsanity.com/paper/2204.09333/) (2022)

## Abstract (arXiv)

Session-based recommendation targets next-item prediction by exploiting user behaviors within a short time period. Compared with other recommendation paradigms, session-based recommendation suffers more from the problem of data sparsity due to the very limited short-term interactions. Self-supervised learning, which can discover ground-truth samples from the raw data, holds vast potentials to tackle this problem. However, existing self-supervised recommendation models mainly rely on item/segment dropout to augment data, which are not fit for session-based recommendation because the dropout leads to sparser data, creating unserviceable self-supervision signals. In this paper, for informative session-based data augmentation, we combine self-supervised learning with co-training, and then develop a framework to enhance session-based recommendation. Technically, we first exploit the session-based graph to augment two views that exhibit the internal and external connectivities of sessions, and then we build two distinct graph encoders over the two views, which recursively leverage the different connectivity information to generate ground-truth samples to supervise each other by contrastive learning. In contrast to the dropout strategy, the proposed self-supervised graph co-training preserves the complete session information and fulfills genuine data augmentation. Extensive experiments on multiple benchmark datasets show that, session-based recommendation can be remarkably enhanced under the regime of self-supervised graph co-training, achieving the state-of-the-art performance.