research-article

Exploiting Explicit and Implicit Item relationships for Session-based Recommendation

Authors:

Julian McAuley,

Guandong XuAuthors Info & Claims

WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

Pages 553 - 561

https://doi.org/10.1145/3539597.3570432

Published: 27 February 2023 Publication History

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.

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Cited By

An GSun JYang YSun F(2024)Enhancing Collaborative Information with Contrastive Learning for Session-based RecommendationInformation Processing & Management10.1016/j.ipm.2024.10373861:4(103738)Online publication date: Jul-2024
https://doi.org/10.1016/j.ipm.2024.103738
Xu YLi TYang YChen WYue L(2024)An adaptive category-aware recommender based on dual knowledge graphsInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10363661:3Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103636
Zhang XXu BMa FLi CLin YLin H(2023)Bi-preference Learning Heterogeneous Hypergraph Networks for Session-based RecommendationACM Transactions on Information Systems10.1145/363194042:3(1-28)Online publication date: 29-Dec-2023
https://dl.acm.org/doi/10.1145/3631940
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Index Terms

Exploiting Explicit and Implicit Item relationships for Session-based Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

February 2023

1345 pages

ISBN:9781450394079

DOI:10.1145/3539597

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Hady Lauw
Singapore Management University
,
Program Chairs:
Luo Si
Salesforce
,
Evimaria Terzi
Boston University
,
Panayiotis Tsaparas
University of Ioannina

Copyright © 2023 ACM.

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Published: 27 February 2023

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WSDM '23

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WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining

February 27 - March 3, 2023

Singapore, Singapore

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Cited By

An GSun JYang YSun F(2024)Enhancing Collaborative Information with Contrastive Learning for Session-based RecommendationInformation Processing & Management10.1016/j.ipm.2024.10373861:4(103738)Online publication date: Jul-2024
https://doi.org/10.1016/j.ipm.2024.103738
Xu YLi TYang YChen WYue L(2024)An adaptive category-aware recommender based on dual knowledge graphsInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10363661:3Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103636
Zhang XXu BMa FLi CLin YLin H(2023)Bi-preference Learning Heterogeneous Hypergraph Networks for Session-based RecommendationACM Transactions on Information Systems10.1145/363194042:3(1-28)Online publication date: 29-Dec-2023
https://dl.acm.org/doi/10.1145/3631940
Yu DLi QYin HXu GFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Causality-guided Graph Learning for Session-based RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614803(3083-3093)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614803
Qiao SZhou WWen JZhang HGao MFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Bi-channel Multiple Sparse Graph Attention Networks for Session-based RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614791(2075-2084)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614791
Yin MWang HXu XWu LZhao SGuo WLiu YTang RLian DChen EFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614781(3009-3019)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614781

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