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Popularity Debiasing from Exposure to Interaction in Collaborative Filtering

Authors:

Xueqi ChengAuthors Info & Claims

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

Pages 1801 - 1805

https://doi.org/10.1145/3539618.3591947

Published: 18 July 2023 Publication History

Abstract

Recommender systems often suffer from popularity bias, where popular items are overly recommended while sacrificing unpopular items. Existing researches generally focus on ensuring the number of recommendations (exposure) of each item is equal or proportional, using inverse propensity weighting, causal intervention, or adversarial training. However, increasing the exposure of unpopular items may not bring more clicks or interactions, resulting in skewed benefits and failing in achieving real reasonable popularity debiasing. In this paper, we propose a new criterion for popularity debiasing, i.e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion. Under the guidance of the criterion, we then propose a debiasing framework with IPL regularization term which is theoretically shown to achieve a win-win situation of both popularity debiasing and recommendation performance. Experiments conducted on four public datasets demonstrate that when equipping two representative collaborative filtering models with our framework, the popularity bias is effectively alleviated while maintaining the recommendation performance.

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Cited By

Lin SGao CChen JZhou SHu BFeng YChen CWang CNejdl WAuer SCha 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
Han XMeng XZhang Y(2025)Exploiting multiple influence pattern of event organizer for event recommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10396662:2Online publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1016/j.ipm.2024.103966

Index Terms

Popularity Debiasing from Exposure to Interaction in Collaborative Filtering
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering

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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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National Key R&D Program of China
Beijing Academy of Artificial Intelligence (BAAI)
National 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

Lin SGao CChen JZhou SHu BFeng YChen CWang CNejdl WAuer SCha 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
Han XMeng XZhang Y(2025)Exploiting multiple influence pattern of event organizer for event recommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10396662:2Online publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1016/j.ipm.2024.103966

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