Exploiting explicit and implicit item relationships for session-based recommendation

Zihao LI, Xianzhi WANG, Chao YANG, Lina YAO, Julian MCAULEY, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

6 Citations (Scopus)

Abstract

The session-based recommendation aims to predict users' immediate next actions based on their short-term behaviors reflected by past and ongoing sessions. Graph neural networks (GNNs) recently dominated the related studies, yet their performance heavily relies on graph structures, which are often predefined, task-specific, and designed heuristically. Furthermore, existing graph-based methods either neglect implicit correlations among items or consider explicit and implicit relationships altogether in the same graphs. We propose to decouple explicit and implicit relationships among items. As such, we can capture the prior knowledge encapsulated in explicit dependencies and learned implicit correlations among items simultaneously in a flexible and more interpretable manner for effective recommendations. We design a dual graph neural network that leverages the feature representations extracted by two GNNs: a graph neural network with a single gate (SG-GNN) and an adaptive graph neural network (A-GNN). The former models explicit dependencies among items. The latter employs a self-learning strategy to capture implicit correlations among items. Our experiments on four real-world datasets show our model outperforms state-of-the-art methods by a large margin, achieving 18.46% and 70.72% improvement in HR@20, and 49.10% and 115.29% improvement in MRR@20 on Diginetica and LastFM datasets. Copyright © 2023 Association for Computing Machinery.

Original languageEnglish
Title of host publicationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages553-561
ISBN (Electronic)9781450394079
DOIs
Publication statusPublished - Feb 2023

Citation

Li, Z., Wang, X., Yang, C., Yao, L., McAuley, J., & Xu, G. (2023). Exploiting explicit and implicit item relationships for session-based recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (pp. 553-561). Association for Computing Machinery. https://doi.org/10.1145/3539597.3570432

Keywords

  • Session-based recommendation
  • Graph neural network
  • Explicit and implicit relationships

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