Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation

Kun Lin, Masoud Mansoury, Farzad Eskandanian, Milad Sabouri, Bamshad Mobasher

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

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Abstract

Calibration in recommender systems is an important performance criterion that ensures consistency between the distribution of user preference categories and that of recommendations generated by the system. Standard methods for mitigating miscalibration typically assume that user preference profiles are static, and they measure calibration relative to the full history of user’s interactions, including possibly outdated and stale preference categories. We conjecture that this approach can lead to recommendations that, while appearing calibrated, in fact, distort users’ true preferences. In this paper, we conduct a preliminary investigation of recommendation calibration at a more granular level, taking into account evolving user preferences. By analyzing differently sized training time windows from the most recent interactions to the oldest, we identify the most relevant segment of user’s preferences that optimizes the calibration metric. We perform an exploratory analysis with datasets from different domains with distinctive user-interaction characteristics. We demonstrate how the evolving nature of user preferences affects recommendation calibration, and how this effect is manifested differently depending on the characteristics of the data in a given domain. Datasets, codes, and more detailed experimental results are available at: https://github.com/nicolelin13/DynamicCalibrationUMAP
Original languageEnglish
Title of host publicationUMAP Adjunct '24
Subtitle of host publicationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York, NY
PublisherACM
Pages86-91
Number of pages6
ISBN (Electronic)979-8-4007-0466-6
DOIs
Publication statusPublished - 2024
Event32nd ACM Conference on User Modeling, Adaptation and Personalization - Cagliari, Italy
Duration: 1 Jul 20244 Jul 2024
Conference number: 32
https://www.um.org/umap2024/

Conference

Conference32nd ACM Conference on User Modeling, Adaptation and Personalization
Abbreviated titleUMAP 2024
Country/TerritoryItaly
CityCagliari
Period1/07/244/07/24
Internet address

Keywords

  • calibration
  • preference dynamics
  • recommender systems

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