short-paper

Neutralizing Popularity Bias in Recommendation Models

Authors:

Yewang ChenAuthors Info & Claims

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

Pages 2623 - 2628

https://doi.org/10.1145/3477495.3531907

Published: 07 July 2022 Publication History

Abstract

Most existing recommendation models learn vectorized representations for items, i.e., item embeddings to make predictions. Item embeddings inherit popularity bias from the data, which leads to biased recommendations. We use this observation to design two simple and effective strategies, which can be flexibly plugged into different backbone recommendation models, to learn popularity neutral item representations. One strategy isolates popularity bias in one embedding direction and neutralizes the popularity direction post-training. The other strategy encourages all embedding directions to be disentangled and popularity neutral. We demonstrate that the proposed strategies outperform state-of-the-art debiasing methods on various real-world datasets, and improve recommendation quality of shallow and deep backbone models.

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Presentation video - Item embeddings inherit popularity bias from the data, which leads to biased recommendations. We use this observation to design two simple and effective strategies, which can be flexibly plugged into different backbone recommendation models, to learn popularity neutral item representations. We demonstrate that the proposed strategies outperform state-of-the-art debiasing methods on various real-world datasets, and improve recommendation quality of shallow and deep backbone models.

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Cited By

Lin SGao CChen JZhou SHu BFeng YChen CWang CNejdl WAuer SKarras OCha MMoens MNajork M(2025)How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral PerspectiveProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703579(659-668)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703579
Coppolillo EMinici MRitacco ECaroprese LPisani FManco G(2024)Balanced Quality Score: Measuring Popularity Debiasing in RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365004315:4(1-27)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1145/3650043
Ning WCheng RYan XKao BHuo NHaldar NTang BChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Debiasing Recommendation with Personal PopularityProceedings of the ACM Web Conference 202410.1145/3589334.3645421(3400-3409)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645421
Show More Cited By

Index Terms

Neutralizing Popularity Bias in Recommendation Models
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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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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Publication History

Published: 07 July 2022

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Youth Innovation Fund of Xiamen
Natural Science Foundation of Fujian Province China
Alibaba Innovative Research Program
Natural Science Foundation of China

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SIGIR '22

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SIGIR

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

Lin SGao CChen JZhou SHu BFeng YChen CWang CNejdl WAuer SKarras OCha MMoens MNajork M(2025)How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral PerspectiveProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703579(659-668)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703579
Coppolillo EMinici MRitacco ECaroprese LPisani FManco G(2024)Balanced Quality Score: Measuring Popularity Debiasing in RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365004315:4(1-27)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1145/3650043
Ning WCheng RYan XKao BHuo NHaldar NTang BChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Debiasing Recommendation with Personal PopularityProceedings of the ACM Web Conference 202410.1145/3589334.3645421(3400-3409)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645421
Wang XWang WFeng FRong WYin CXiong Z(2024)Causal Intervention for Fairness in Multibehavior RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.337990311:5(6320-6332)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3379903
Wang CLiu AQin T(2024)Learning-to-rank debias with popularity-weighted negative sampling and popularity regularizationNeurocomputing10.1016/j.neucom.2024.127681587(127681)Online publication date: Jun-2024
https://doi.org/10.1016/j.neucom.2024.127681
Okamura HMaeda KTogo ROgawa THaseyama M(2024)Flexibly manipulating popularity bias for tackling trade-offs in recommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10360661:2Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103606
Li XXu ZPeng HWang HHuang Q(2023)Personalized News Recommendations Based on NRMSProceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)10.2991/978-94-6463-198-2_16(137-148)Online publication date: 26-Jul-2023
https://doi.org/10.2991/978-94-6463-198-2_16
Zhao JWang WLin XQu LZhang JChua TFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Popularity-aware Distributionally Robust Optimization for Recommendation SystemProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615492(4967-4973)Online publication date: 21-Oct-2023
https://doi.org/10.1145/3583780.3615492
Chen YYe WXv GLin CZhu XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)TCCM: Time and Content-Aware Causal Model for Unbiased News RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615272(3778-3782)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615272
Jing JZhang YZhou XShen ZFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614801(1014-1024)Online publication date: 21-Oct-2023
https://doi.org/10.1145/3583780.3614801
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