research-article

Modeling Cross-session Information with Multi-interest Graph Neural Networks for the Next-item Recommendation

Authors:

Chiao-Ting Chen,

Szu-Hao HuangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 1

Article No.: 1, Pages 1 - 28

https://doi.org/10.1145/3532192

Published: 20 February 2023 Publication History

Abstract

Next-item recommendation involves predicting the next item of interest of a given user from their past behavior. Users tend to browse and purchase various items on e-commerce websites according to their varied interests and needs, as reflected in their purchasing history. Most existing next-item recommendation methods aim at extracting the main point of interest in each browsing session and encapsulate it in a single representation. However, past behavior sequences reflect the multiple interests of a single user, which cannot be captured by methods that focus on single-interest contexts. Indeed, multiple interests cannot be captured in a single representation, and doing so results in missing information. Therefore, we propose a model with a multi-interest structure for capturing the various interests of users from their behavior sequence. Moreover, we adopted a method based on a graph neural network to construct interest graphs based on the historical and current behavior sequences of users. These graphs can capture complex item transition patterns related to different interests. In experiments, the proposed method outperforms state-of-the-art session-based recommendation systems on three real-world datasets, achieving 4% improvement of Recall over the SOTAs on Jdata dataset.

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Index Terms

Modeling Cross-session Information with Multi-interest Graph Neural Networks for the Next-item Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 1

January 2023

375 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3572846

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 20 February 2023

Online AM: 27 April 2022

Accepted: 14 April 2022

Revised: 14 March 2022

Received: 02 November 2021

Published in TKDD Volume 17, Issue 1

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Yuanta Securties and the Financial Technology (FinTech) Innovation Research Center, National Yang Ming Chiao Tung University
Ministry of Science and Technology, Taiwan

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Rong HQian MMa TJin DSheng V(2024)CoBjeason: Reasoning Covered Object in Image by Multi-Agent Collaboration Based on Informed Knowledge GraphACM Transactions on Knowledge Discovery from Data10.1145/364356518:5(1-56)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3643565
Chen CLee CHuang SPeng W(2024)Credit Card Fraud Detection via Intelligent Sampling and Self-supervised LearningACM Transactions on Intelligent Systems and Technology10.1145/364128315:2(1-29)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3641283
Ye ZXie XAi QLiu YWang ZSu WZhang M(2024)Relevance Feedback with Brain SignalsACM Transactions on Information Systems10.1145/363787442:4(1-37)Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1145/3637874
Liu JChen CLee CHuang S(2024)Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation SystemACM Transactions on Intelligent Systems and Technology10.1145/363527315:2(1-26)Online publication date: 28-Mar-2024
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Lin SChen CHuang S(2024)Member-Augmented Group Recommendation With Multi-Interest Framework and Knowledge Graph EmbeddingsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.332273211:3(3193-3206)Online publication date: Jun-2024
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Shi F(2024)Construction of English Resource Database Network Information Recommendation Model Based on LSTM Algorithm2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT)10.1109/ICDCOT61034.2024.10516163(1-5)Online publication date: 15-Mar-2024
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Lin RTang FYuan CZhong HLi WTang Y(2024)DeHier: decoupled and hierarchical graph neural networks for multi-interest session-based recommendationWorld Wide Web10.1007/s11280-024-01294-z28:1Online publication date: 22-Nov-2024
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