Jointly modeling intra- and inter-session dependencies with graph neural networks for session-based recommendations

Jingjing WANG, Haoran XIE, Fu Lee WANG*, Lap-Kei LEE, Mingqiang WEI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

16 Citations (Scopus)


Recently, graph neural networks (GNNs) have achieved promising results in session-based recommendation. Existing methods typically construct a local session graph and a global session graph to explore complex item transition patterns. However, studies have seldom investigated the repeat consumption phenomenon in a local graph. In addition, it is challenging to retrieve relevant adjacent nodes from the whole training set owing to computational complexity and space constraints. In this study, we use a GNN to jointly model intra- and inter-session item dependencies for session-based recommendations. We construct a repeat-aware local session graph to encode the intra-item dependencies and generate the session representation with positional awareness. Then, we use sessions from the current mini-batch instead of the whole training set to construct a global graph, which we refer to as the session-level global graph. Next, we aggregate the K-nearest neighbors to generate the final session representation, which enables easy and efficient neighbor searching. Extensive experiments on three real-world recommendation datasets demonstrate that RN-GNN outperforms state-of-the-art methods.
Original languageEnglish
Article number103209
JournalInformation Processing and Management
Issue number2
Early online date7 Dec 2022
Publication statusPublished - 1 Mar 2023

Bibliographical note

Funding Information:
The research has been supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China ( UGC/FDS16/E01/19 ), and the Direct Grant ( DR22A2 ), Lam Woo Research Fund ( LWP20019 ) and the Faculty Research Grants ( DB22B4 and DB22B7 ) of Lingnan University, Hong Kong SAR, China .

Publisher Copyright:
© 2022 The Authors


  • Session-based recommendation
  • Graph attention neural networks
  • Reverse-position


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