# DGTN: Dual-channel Graph Transition Network for Session-based Recommendation

arXiv: https://arxiv.org/abs/2009.10002 · HTML: https://mlsanity.com/paper/2009.10002/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 8 of 8)

- [Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation](https://mlsanity.com/paper/2402.14473/) (2024)
- [DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2209.10163/) (2022)
- [Introducing Self-Attention to Target Attentive Graph Neural Networks](https://mlsanity.com/paper/2107.01516/) (2021)
- [ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training](https://mlsanity.com/paper/2501.07078/) (2025)
- [Graph Spring Network and Informative Anchor Selection for Session-based Recommendation](https://mlsanity.com/paper/2202.09502/) (2022)
- [SR-PredictAO: Session-based Recommendation with High-Capability Predictor Add-On](https://mlsanity.com/paper/2309.12218/) (2023)
- [Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution Network](https://mlsanity.com/paper/2302.03221/) (2023)
- [Potent but Stealthy: Rethink Profile Pollution against Sequential Recommendation via Bi-level Constrained Reinforcement Paradigm](https://mlsanity.com/paper/2511.09392/) (2025)

## Abstract (arXiv)

The task of session-based recommendation is to predict user actions based on anonymous sessions. Recent research mainly models the target session as a sequence or a graph to capture item transitions within it, ignoring complex transitions between items in different sessions that have been generated by other users. These item transitions include potential collaborative information and reflect similar behavior patterns, which we assume may help with the recommendation for the target session. In this paper, we propose a novel method, namely Dual-channel Graph Transition Network (DGTN), to model item transitions within not only the target session but also the neighbor sessions. Specifically, we integrate the target session and its neighbor (similar) sessions into a single graph. Then the transition signals are explicitly injected into the embedding by channel-aware propagation. Experiments on real-world datasets demonstrate that DGTN outperforms other state-of-the-art methods. Further analysis verifies the rationality of dual-channel item transition modeling, suggesting a potential future direction for session-based recommendation.