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GraphFusionSBR: Denoising Multi-Channel Graphs for Session-Based Recommendation

arXiv 2601.08497 ↗First posted 13 Jan 2026cs.IR cs.LGcited by 0

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Abstract (arXiv)

Session-based recommendation systems must capture implicit user intents from sessions. However, existing models suffer from issues such as item interaction dominance and noisy sessions. We propose a multi-channel recommendation model, including a knowledge graph channel, a session hypergraph channel, and a session line graph channel, to capture information from multiple sources. Our model adaptively removes redundant edges in the knowledge graph channel to reduce noise. Knowledge graph representations cooperate with hypergraph representations for prediction to alleviate item dominance. We also generate in-session attention for denoising. Finally, we maximize mutual information between the hypergraph and line graph channels as an auxiliary task. Experiments demonstrate that our method enhances the accuracy of various recommendations, including e-commerce and multimedia recommendations. We release the code on GitHub for reproducibility.footnotehttps://github.com/hohehohe0509/DSR-HK

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 13 Jan 2026.

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16 of 28 references.

References · 16

  1. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  2. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  3. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,148
  4. 2015Session-based Recommendations with Recurrent Neural Networkscited by 695
  5. 2018Hypergraph Neural Networkscited by 488
  6. 2020Parameterized Explainer for Graph Neural Networkcited by 426
  7. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 343
  8. 2018Session-based Recommendation with Graph Neural Networkscited by 312
  9. 2017Neural Attentive Session-based Recommendationcited by 237
  10. 2019Knowledge Graph Convolutional Networks for Recommender Systemscited by 121
  11. 2021Global Context Enhanced Graph Neural Networks for Session-based Recommendationcited by 81
  12. 2020Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendationcited by 75
4 more
  1. 2022Knowledge Graph Contrastive Learning for Recommendationcited by 70
  2. 2021Self-Supervised Graph Co-Training for Session-based Recommendationcited by 43
  3. 2021Session-based Recommendation with Hypergraph Attention Networkscited by 19
  4. 2020Rethinking Item Importance in Session-based Recommendationcited by 7

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