mlsanityData to 7 Oct 2026

SR-HetGNN:Session-based Recommendation with Heterogeneous Graph Neural Network

arXiv 2108.05641 ↗First posted 12 Aug 2021cs.IR cs.AIcited by 0

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Abstract (arXiv)

The Session-Based Recommendation System aims to predict the user's next click based on their previous session sequence. The current studies generally learn user preferences according to the transitions of items in the user's session sequence. However, other effective information in the session sequence, such as user profiles, are largely ignored which may lead to the model unable to learn the user's specific preferences. In this paper, we propose SR-HetGNN, a novel session recommendation method that uses a heterogeneous graph neural network (HetGNN) to learn session embeddings and capture the specific preferences of anonymous users. Specifically, SR-HetGNN first constructs heterogeneous graphs containing various types of nodes according to the session sequence, which can capture the dependencies among items, users, and sessions. Second, HetGNN captures the complex transitions between items and learns the item embeddings containing user information. Finally, local and global session embeddings are combined with the attentional network to obtain the final session embedding, considering the influence of users' long and short-term preferences. SR-HetGNN is shown to be superior to the existing state-of-the-art session-based recommendation methods through extensive experiments over two real large datasets Diginetica and Tmall.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 40 references that are in the map. First posted on arXiv 12 Aug 2021.

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18 of 40 references.

References · 18

  1. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  2. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  3. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  4. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  5. 2015Session-based Recommendations with Recurrent Neural Networkscited by 685
  6. 2015LINE: Large-scale Information Network Embeddingcited by 582
  7. 2018Session-based Recommendation with Graph Neural Networkscited by 312
  8. 2017Neural Attentive Session-based Recommendationcited by 237
  9. 2017Graph Embedding Techniques, Applications, and Performance: A Surveycited by 188
  10. 2017A Survey on Network Embeddingcited by 142
  11. 2019A Survey on Session-based Recommender Systemscited by 97
  12. 2021Global Context Enhanced Graph Neural Networks for Session-based Recommendationcited by 81
6 more
  1. 2020Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendationcited by 75
  2. 2020Interpretable and Efficient Heterogeneous Graph Convolutional Networkcited by 48
  3. 2020TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendationcited by 33
  4. 2021Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networkscited by 20
  5. 2021Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Networkcited by 5
  6. 2021Session-based Recommendation with Self-Attention Networkscited by 3

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