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Robust Preference-Guided Denoising for Graph based Social Recommendation

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Yong LiAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 1097 - 1108

https://doi.org/10.1145/3543507.3583374

Published: 30 April 2023 Publication History

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Abstract

Graph Neural Network (GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations. However, in terms of both effectiveness and efficiency of recommendation, a large portion of social relations can be redundant or even noisy, e.g., it is quite normal that friends share no preference in a certain domain. Existing models do not fully solve this problem of relation redundancy and noise, as they directly characterize social influence over the full social network. In this paper, we instead propose to improve graph based social recommendation by only retaining the informative social relations to ensure an efficient and effective influence diffusion, i.e., graph denoising. Our designed denoising method is preference-guided to model social relation confidence and benefits user preference learning in return by providing a denoised but more informative social graph for recommendation models. Moreover, to avoid interference of noisy social relations, it designs a self-correcting curriculum learning module and an adaptive denoising strategy, both favoring highly-confident samples. Experimental results on three public datasets demonstrate its consistent capability of improving three state-of-the-art social recommendation models by robustly removing 10-40% of original relations. We release the source code at https://github.com/tsinghua-fib-lab/Graph-Denoising-SocialRec.

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Sharma KLee YNambi SSalian AShah SKim SKumar S(2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
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Index Terms

Robust Preference-Guided Denoising for Graph based Social Recommendation
1. Computing methodologies
2. Human-centered computing
  1. Collaborative and social computing

Index terms have been assigned to the content through auto-classification.

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WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

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Published: 30 April 2023

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Cited By

Lu JWu ZChen ZCai ZWang SCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Towards Multi-view Consistent Graph DiffusionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681258(186-195)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681258
Sharma KLee YNambi SSalian AShah SKim SKumar S(2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3661821
Chua HDu YSun ZWang ZZhang JOng Y(2024)Unified Denoising Training for RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688109(612-621)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688109
Sun YSun ZDu YZhang JOng YBaeza-Yates RBonchi F(2024)Self-Supervised Denoising through Independent Cascade Graph Augmentation for Robust Social RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671958(2806-2817)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671958
Li JWang HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Graph Diffusive Self-Supervised Learning for Social RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657962(2442-2446)Online publication date: 10-Jul-2024
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Zhao JWenjie WXu YSun TFeng FChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Denoising Diffusion Recommender ModelProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657825(1370-1379)Online publication date: 10-Jul-2024
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Li NBan XLing CGao CHu LJiang PGai KLi YLiao QHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Modeling User Fatigue for Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657802(996-1005)Online publication date: 10-Jul-2024
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Ma WWang YZhu YWang ZJing MZhao XYu JTang FAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)MADM: A Model-agnostic Denoising Module for Graph-based Social RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635784(501-509)Online publication date: 4-Mar-2024
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Luo LWang MLiu JHuang J(2024)Inhomogeneous Interest Modeling via Hypergraph Convolutional Networks for Social Recommendation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651102(1-9)Online publication date: 30-Jun-2024
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