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Measuring Item Global Residual Value for Fair Recommendation

Authors:

Peng JiangAuthors Info & Claims

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

Pages 269 - 278

https://doi.org/10.1145/3539618.3591724

Published: 18 July 2023 Publication History

Abstract

In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various recommendation systems are designed not only to satisfy users' personalized information needs but also to allocate items' exposure. However, recent recommendation studies mainly focus on modeling user preferences to present satisfying results and maximize user interactions, while paying little attention to developing item-side fair exposure mechanisms for rational information delivery. This may lead to serious resource allocation problems on the item side, such as the Snowball Effect. Furthermore, unfair exposure mechanisms may hurt recommendation performance. In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items. We first conduct empirical analyses of feed scenarios to explore exposure problems between items with distinct uploaded times. This points out that unfair exposure caused by the time factor may be the major cause of the Snowball Effect. Then, we propose to explicitly model item-level customized timeliness distribution, Global Residual Value (GRV), for fair resource allocation. This GRV module is introduced into recommendations with the designed Timeliness-aware Fair Recommendation Framework (TaFR). Extensive experiments on two datasets demonstrate that TaFR achieves consistent improvements with various backbone recommendation models. By modeling item-side customized Global Residual Value, we achieve a fairer distribution of resources and, at the same time, improve recommendation performance.

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Cited By

Dong LHe GSun A(2024)Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval BiasProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688113(179-188)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688113
Zeng HHe ZYue ZMcAuley JWang DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Fair Sequential Recommendation without User DemographicsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657703(395-404)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657703
Wang YSun PMa WZhang MZhang YJiang PMa SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Intersectional Two-sided Fairness in RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645518(3609-3620)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645518

Index Terms

Measuring Item Global Residual Value for Fair Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Information & Contributors

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Published In

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

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2023

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the Natural Science Foundation of China
the Natural Science Foundation of China

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SIGIR '23

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SIGIR

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

Dong LHe GSun A(2024)Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval BiasProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688113(179-188)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688113
Zeng HHe ZYue ZMcAuley JWang DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Fair Sequential Recommendation without User DemographicsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657703(395-404)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657703
Wang YSun PMa WZhang MZhang YJiang PMa SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Intersectional Two-sided Fairness in RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645518(3609-3620)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645518

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