short-paper

Adversarial Graph Perturbations for Recommendations at Scale

Authors:

Kwei-Herng Lai,

Hao YangAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1854 - 1858

https://doi.org/10.1145/3477495.3531763

Published: 07 July 2022 Publication History

Abstract

Graph Neural Networks (GNNs) provide a class of powerful architectures that are effective for graph-based collaborative filtering. Nevertheless, GNNs are known to be vulnerable to adversarial perturbations. Adversarial training is a simple yet effective way to improve the robustness of neural models. For example, many prior studies inject adversarial perturbations into either node features or hidden layers of GNNs. However, perturbing graph structures has been far less studied in recommendations.

To bridge this gap, we propose AdvGraph to model adversarial graph perturbations during the training of GNNs. Our AdvGraph is mainly based on min-max robust optimization, where an universal graph perturbation is obtained through an inner maximization while the outer optimization aims to compute the model parameters of GNNs. However, direct optimizing the inner problem is challenging due to discrete nature of the graph perturbations. To address this issue, an unbiased gradient estimator is further proposed to compute the gradients of discrete variables. Extensive experiments demonstrate that our AdvGraph is able to enhance the generalization performance of GNN-based recommenders.

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Adversarial Graph Perturbations for Recommendations at Scale

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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Published: 07 July 2022

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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https://dl.acm.org/doi/10.1145/3637528.3671781
Wang JChen GXin KFei Z(2024)Metric learning with adversarial hard negative samples for tag recommendationThe Journal of Supercomputing10.1007/s11227-024-06274-880:14(21475-21507)Online publication date: 11-Jun-2024
https://doi.org/10.1007/s11227-024-06274-8
Lai VChen HYeh CXu MCai YYang H(2023)Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608831(791-797)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608831
Chen HZhou KLai KYeh CZheng YHu XYang H(2023)Hessian-aware Quantized Node Embeddings for RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608826(757-762)Online publication date: 14-Sep-2023
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