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Distributionally Robust Graph-based Recommendation System

Authors:

Can WangAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 3777 - 3788

https://doi.org/10.1145/3589334.3645598

Published: 13 May 2024 Publication History

Abstract

With the capacity to capture high-order collaborative signals, Graph Neural Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their efficacy often hinges on the assumption that training and testing data share the same distribution (\aka IID assumption), and exhibits significant declines under distribution shifts. Distribution shifts commonly arises in RS, often attributed to the dynamic nature of user preferences or ubiquitous biases during data collection in RS. Despite its significance, researches on GNN-based recommendation against distribution shift are still sparse. To bridge this gap, we propose Distributionally Robust GNN (DR-GNN) that incorporates Distributional Robust Optimization (DRO) into the GNN-based recommendation. DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support. Notably, while DR-GNN involves complex optimization, it can be implemented easily and efficiently. Our extensive experiments validate the effectiveness of DR-GNN against three typical distribution shifts. The code is available at https://github.com/WANGBohaO-jpg/DR-GNN.

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Cited By

Amara AHadj Taieb MBen Aouicha M(2025)A multi-view GNN-based network representation learning framework for recommendation systemsNeurocomputing10.1016/j.neucom.2024.129001619(129001)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129001
Lin SZhou SChen JFeng YShi QChen CLi YWang C(2024)ReCRec: Reasoning the Causes of Implicit Feedback for Debiased RecommendationACM Transactions on Information Systems10.1145/367227542:6(1-26)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3672275
Cui YLiu FWang PWang BTang HWan YWang JChen J(2024)Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language ModelsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688118(507-517)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688118
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Index Terms

Distributionally Robust Graph-based Recommendation System
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

Copyright © 2024 ACM.

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Published: 13 May 2024

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Supercomputing Center of Hangzhou City University
National Natural Science Foundation of China
Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study

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WWW '24

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SIGWEB

WWW '24: The ACM Web Conference 2024

May 13 - 17, 2024

Singapore, Singapore

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Amara AHadj Taieb MBen Aouicha M(2025)A multi-view GNN-based network representation learning framework for recommendation systemsNeurocomputing10.1016/j.neucom.2024.129001619(129001)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129001
Lin SZhou SChen JFeng YShi QChen CLi YWang C(2024)ReCRec: Reasoning the Causes of Implicit Feedback for Debiased RecommendationACM Transactions on Information Systems10.1145/367227542:6(1-26)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3672275
Cui YLiu FWang PWang BTang HWan YWang JChen J(2024)Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language ModelsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688118(507-517)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688118
Yılmaz Yİşlek İÖğüdücü Ş(2024)Topic Modeling Enhanced Tripartite Graph for Recommendation Using Metapaths2024 9th International Conference on Computer Science and Engineering (UBMK)10.1109/UBMK63289.2024.10773526(1144-1149)Online publication date: 26-Oct-2024
https://doi.org/10.1109/UBMK63289.2024.10773526
Chen HYang YBei YWang ZXu YHuang F(2024)Graph Neural Patching for Cold-Start RecommendationsDatabases Theory and Applications10.1007/978-981-96-1242-0_25(334-346)Online publication date: 13-Dec-2024
https://doi.org/10.1007/978-981-96-1242-0_25

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