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SimCGNN: Simple Contrastive Graph Neural Network for Session-based Recommendation

arXiv 2302.03997 ↗First posted 8 Feb 2023cs.IR cs.AIcited by 0

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Abstract (arXiv)

Session-based recommendation (SBR) problem, which focuses on next-item prediction for anonymous users, has received increasingly more attention from researchers. Existing graph-based SBR methods all lack the ability to differentiate between sessions with the same last item, and suffer from severe popularity bias. Inspired by nowadays emerging contrastive learning methods, this paper presents a Simple Contrastive Graph Neural Network for Session-based Recommendation (SimCGNN). In SimCGNN, we first obtain normalized session embeddings on constructed session graphs. We next construct positive and negative samples of the sessions by two forward propagation and a novel negative sample selection strategy, and then calculate the constructive loss. Finally, session embeddings are used to give prediction. Extensive experiments conducted on two real-word datasets show our SimCGNN achieves a significant improvement over state-of-the-art methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 33 references that are in the map. First posted on arXiv 8 Feb 2023.

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17 of 33 references.

References · 17

  1. 2017Graph Attention Networkscited by 8,854
  2. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  3. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,838
  4. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  5. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
  6. 2015Gated Graph Sequence Neural Networkscited by 792
  7. 2015Session-based Recommendations with Recurrent Neural Networkscited by 685
  8. 2018Session-based Recommendation with Graph Neural Networkscited by 312
  9. 2017Neural Attentive Session-based Recommendationcited by 237
  10. 2016Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendationcited by 152
  11. 2016Improved Recurrent Neural Networks for Session-based Recommendationscited by 86
  12. 2021Global Context Enhanced Graph Neural Networks for Session-based Recommendationcited by 81
5 more
  1. 2020Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendationcited by 75
  2. 2019Rethinking the Item Order in Session-based Recommendation with Graph Neural Networkscited by 43
  3. 2020TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendationcited by 33
  4. 2019NISER: Normalized Item and Session Representations to Handle Popularity Biascited by 12
  5. 2022Disentangled Graph Neural Networks for Session-based Recommendationcited by 8

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