research-article

Adaptive Popularity Debiasing Aggregator for Graph Collaborative Filtering

Authors:

Xiao HuangAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 7 - 17

https://doi.org/10.1145/3539618.3591635

Published: 18 July 2023 Publication History

Abstract

The graph neural network-based collaborative filtering (CF) models user-item interactions as a bipartite graph and performs iterative aggregation to enhance performance. Unfortunately, the aggregation process may amplify the popularity bias, which impedes user engagement with niche (unpopular) items. While some efforts have studied the popularity bias in CF, they often focus on modifying loss functions, which can not fully address the popularity bias in GNN-based CF models. This is because the debiasing loss can be falsely backpropagated to non-target nodes during the backward pass of the aggregation.

In this work, we study whether we can fundamentally neutralize the popularity bias in the aggregation process of GNN-based CF models. This is challenging because 1) estimating the effect of popularity is difficult due to the varied popularity caused by the aggregation from high-order neighbors, and 2) it is hard to train learnable popularity debiasing aggregation functions because of data sparsity. To this end, we theoretically analyze the cause of popularity bias and propose a quantitative metric, named inverse popularity score, to measure the effect of popularity in the representation space. Based on it, a novel graph aggregator named APDA is proposed to learn per-edge weight to neutralize popularity bias in aggregation. We further strengthen the debiasing effect with a weight scaling mechanism and residual connections. We apply APDA to two backbones and conduct extensive experiments on three real-world datasets. The results show that APDA significantly outperforms the state-of-the-art baselines in terms of recommendation performance and popularity debiasing.

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Cited By

Wang XFukumoto FCui JSuzuki YLi JYu DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)CaDRec: Contextualized and Debiased Recommender ModelProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657799(405-415)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657799
Chen JWenjie WGao CWu PWei JHua QHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Treatment Effect Estimation for User Interest Exploration on Recommender SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657736(1861-1871)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657736
Zhang AMa WWei PSheng LWang XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph DropoutProceedings of the ACM on Web Conference 202410.1145/3589334.3645667(3864-3875)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645667
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Index Terms

Adaptive Popularity Debiasing Aggregator for Graph Collaborative Filtering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

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Published: 18 July 2023

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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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

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Cited By

Wang XFukumoto FCui JSuzuki YLi JYu DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)CaDRec: Contextualized and Debiased Recommender ModelProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657799(405-415)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657799
Chen JWenjie WGao CWu PWei JHua QHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Treatment Effect Estimation for User Interest Exploration on Recommender SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657736(1861-1871)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657736
Zhang AMa WWei PSheng LWang XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph DropoutProceedings of the ACM on Web Conference 202410.1145/3589334.3645667(3864-3875)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645667
Wang BChen JLi CZhou SShi QGao YFeng YChen CWang CChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Distributionally Robust Graph-based Recommendation SystemProceedings of the ACM on Web Conference 202410.1145/3589334.3645598(3777-3788)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645598
Zhang YZhang YYan DHe QYang Y(2024)NIE-GCN: Neighbor Item Embedding-Aware Graph Convolutional Network for RecommendationIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.335065854:5(2810-2821)Online publication date: May-2024
https://doi.org/10.1109/TSMC.2024.3350658
Chuang YWang GChang CLai KZha DTang RYang FReyes AZhou KJiang XHu XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical ResearchProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614739(5021-5025)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614739

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