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SuGeR: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation

Authors:

Zhenning Zhang,

Hanghang TongAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 4712 - 4716

https://doi.org/10.1145/3511808.3557707

Published: 17 October 2022 Publication History

Abstract

Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users. Although Graph Neural Networks (GNNs) have been applied to this problem and achieved superior performance, existing methods underexplore the graph-level GNN methods, which exhibit great potential in traditional recommender system. Furthermore, they usually lack the transferability from one domain with sufficient supervision to another domain which might suffer from the label scarcity issue. In this work, we propose a subgraph-based Graph Neural Network model, SuGeR, for bundle recommendation to handle these limitations. SuGeR generates heterogeneous subgraphs around the user-bundle pairs and then maps those subgraphs to the users' preference predictions via neural relational graph propagation. Experimental results show that SUGER significantly outperforms the state-of-the-art baselines in the basic and the transfer bundle recommendation tasks by up to 77.17% by NDCG@40. The source code is available at: https://github.com/Zhang-Zhenning/SUGER.

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Cited By

Yang WYang CLi JTan YLu XShi C(2024)Non-autoregressive personalized bundle generationInformation Processing & Management10.1016/j.ipm.2024.10381461:5(103814)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103814
Liang Y(2024)Outlier item detection in bundle recommendation via the attention mechanismHigh-Confidence Computing10.1016/j.hcc.2024.1002004:3(100200)Online publication date: Sep-2024
https://doi.org/10.1016/j.hcc.2024.100200
Du BYuan CBarton RNeiman TTong H(2022)Self-supervised Hypergraph Representation Learning2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020240(505-514)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020240

Index Terms

SuGeR: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Publication History

Published: 17 October 2022

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NIFA
NSF (National Science Foundation)
the NSF Program on Fairness in AI in collaboration with Amazon
DARPA
ARO

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CIKM '22

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

GA, Atlanta, USA

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Yang WYang CLi JTan YLu XShi C(2024)Non-autoregressive personalized bundle generationInformation Processing & Management10.1016/j.ipm.2024.10381461:5(103814)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103814
Liang Y(2024)Outlier item detection in bundle recommendation via the attention mechanismHigh-Confidence Computing10.1016/j.hcc.2024.1002004:3(100200)Online publication date: Sep-2024
https://doi.org/10.1016/j.hcc.2024.100200
Du BYuan CBarton RNeiman TTong H(2022)Self-supervised Hypergraph Representation Learning2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020240(505-514)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020240

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