Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems

Jin Huang, Harrie Oosterhuis, Masoud Mansoury*, Herke Van Hoof, Maarten de Rijke

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Two typical forms of bias in user interaction data with recommender systems (RSs) are popularity bias and positivity bias, which manifest themselves as the over-representation of interactions with popular items or items that users prefer, respectively. Debiasing methods aim to mitigate the effect of selection bias on the evaluation and optimization of RSs. However, existing debiasing methods only consider single-factor forms of bias, e.g., only the item (popularity) or only the rating value (positivity). This is in stark contrast with the real world where user selections are generally affected by multiple factors at once. In this work, we consider multifactorial selection bias in RSs. Our focus is on selection bias affected by both item and rating value factors, which is a generalization and combination of popularity and positivity bias. While the concept of multifactorial bias is intuitive, it brings a severe practical challenge as it requires substantially more data for accurate bias estimation. As a solution, we propose smoothing and alternating gradient descent techniques to reduce variance and improve the robustness of its optimization. Our experimental results reveal that, with our proposed techniques, multifactorial bias corrections are more effective and robust than single-factor counterparts on real-world and synthetic datasets.
Original languageEnglish
Title of host publicationSIGIR '24
Subtitle of host publicationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages416-426
Number of pages11
ISBN (Print)979-8-4007-0431-4
DOIs
Publication statusPublished - 2024
Event47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024 - Washington, United States
Duration: 14 Jul 202418 Jul 2024

Conference

Conference47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024
Country/TerritoryUnited States
CityWashington
Period14/07/2418/07/24

Keywords

  • propensity estimation
  • recommender systems
  • unbiased learning

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