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Controlling Popularity Bias in Learning-to-Rank Recommendation

Authors:

Himan Abdollahpouri,

Bamshad MobasherAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 42 - 46

https://doi.org/10.1145/3109859.3109912

Published: 27 August 2017 Publication History

Abstract

Many recommendation algorithms suffer from popularity bias in their output: popular items are recommended frequently and less popular ones rarely, if at all. However, less popular, long-tail items are precisely those that are often desirable recommendations. In this paper, we introduce a flexible regularization-based framework to enhance the long-tail coverage of recommendation lists in a learning-to-rank algorithm. We show that regularization provides a tunable mechanism for controlling the trade-off between accuracy and coverage. Moreover, the experimental results using two data sets show that it is possible to improve coverage of long tail items without substantial loss of ranking performance.

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Cited By

Qiu YDong HChen JHe X(2024)LightAD: accelerating AutoDebias with adaptive samplingJUSTC10.52396/JUSTC-2022-010054:4(0405)Online publication date: 2024
https://doi.org/10.52396/JUSTC-2022-0100
He LZhao JGu YElbaz MDing Z(2024)A bias study and an unbiased deep neural network for recommender systemsWeb Intelligence10.3233/WEB-23003622:1(15-29)Online publication date: 26-Mar-2024
https://doi.org/10.3233/WEB-230036
Zhao YWang YLiu YCheng XAggarwal CDerr T(2024)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/3664928Online publication date: 21-May-2024
https://doi.org/10.1145/3664928
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Index Terms

Controlling Popularity Bias in Learning-to-Rank Recommendation
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Learning to rank
  2. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
      1. Optimization algorithms
2. Information systems

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Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 27 August 2017

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National Science Foundation

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RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

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RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Qiu YDong HChen JHe X(2024)LightAD: accelerating AutoDebias with adaptive samplingJUSTC10.52396/JUSTC-2022-010054:4(0405)Online publication date: 2024
https://doi.org/10.52396/JUSTC-2022-0100
He LZhao JGu YElbaz MDing Z(2024)A bias study and an unbiased deep neural network for recommender systemsWeb Intelligence10.3233/WEB-23003622:1(15-29)Online publication date: 26-Mar-2024
https://doi.org/10.3233/WEB-230036
Zhao YWang YLiu YCheng XAggarwal CDerr T(2024)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/3664928Online publication date: 21-May-2024
https://doi.org/10.1145/3664928
Naghiaei MDehghan MRahmani HAzizi JAliannejadi MOosterhuis HBast HXiong C(2024)Personalized Beyond-accuracy Calibration in RecommendationProceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3664190.3672507(107-116)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3664190.3672507
Liang SPan Zliu wYin Jde Rijke M(2024)A Survey on Variational Autoencoders in Recommender SystemsACM Computing Surveys10.1145/366336456:10(1-40)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3663364
Malitesta DCornacchia GPomo CMerra FDi Noia TDi Sciascio E(2024)Formalizing Multimedia Recommendation through Multimodal Deep LearningACM Transactions on Recommender Systems10.1145/3662738Online publication date: 29-Apr-2024
https://doi.org/10.1145/3662738
Wang ZZou LLi CWang SChen XYin DLiu W(2024)Toward Bias-Agnostic Recommender Systems: A Universal Generative FrameworkACM Transactions on Information Systems10.1145/365561742:6(1-30)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3655617
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Ahn YLin Y(2024)Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender SystemsACM Transactions on Intelligent Systems and Technology10.1145/365004415:4(1-20)Online publication date: 29-Feb-2024
https://dl.acm.org/doi/10.1145/3650044
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
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