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User-Dependent Learning to Debias for Recommendation

Authors:

Jun WuAuthors Info & Claims

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

Pages 2491 - 2495

https://doi.org/10.1145/3539618.3592083

Published: 18 July 2023 Publication History

Abstract

In recommender systems (RSs), inverse propensity score (IPS) has been a key technique to mitigate popularity bias by decreasing the contribution of popular items in modeling user-item interactions. However, conventional IPS treats all users equally, which tends to over-debias the popularity-insensitive (PI) users and under-debias the popularity-sensitive (PS) users. Furthermore, in such a treatment, IPS only performs slightly well on the debiased test while does not work on the normal biased test. To this end, we propose a user-dependent IPS (UDIPS in short) method, which adaptively conducts propensity estimation for each user-item pair based on the user's sensitivity to item popularity. Like IPS, our theoretical analysis validates the unbiasedness of UDIPS. Remarkably, our solution is model-agnostic and can be easily used to upgrade current unbiased recommenders. We implemented it in four state-of-the-art models for unbiased recommendation, and experimental results on two benchmark datasets demonstrate the effectiveness of our method in both unbiased and normal biased test.

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Cited By

Yang XLiu ZLu XYuan YLu SGao YSerra ESpezzano F(2024)Contrastive Disentangled Representation Learning for Debiasing Recommendation with Uniform DataProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679889(4188-4192)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679889
Klimashevskaia AJannach DElahi MTrattner C(2024)A survey on popularity bias in recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09406-0Online publication date: 1-Jul-2024
https://doi.org/10.1007/s11257-024-09406-0

Index Terms

User-Dependent Learning to Debias for Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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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 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 the author(s) 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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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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Short-paper

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the Fundamental Research Funds for the Central Universities,
the Open Reasearch Fund from the Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of HongKong ShenZhen,

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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

Yang XLiu ZLu XYuan YLu SGao YSerra ESpezzano F(2024)Contrastive Disentangled Representation Learning for Debiasing Recommendation with Uniform DataProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679889(4188-4192)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679889
Klimashevskaia AJannach DElahi MTrattner C(2024)A survey on popularity bias in recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09406-0Online publication date: 1-Jul-2024
https://doi.org/10.1007/s11257-024-09406-0

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