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are static, and they measure calibration relative to the full history of user’s interactions, including possibly outdated and stale preference categories.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 language | English |
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Title of host publication | UMAP Adjunct '24 |
Subtitle of host publication | Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization |
Place of Publication | New York, NY |
Publisher | ACM |
Pages | 86-91 |
Number of pages | 6 |
ISBN (Electronic) | 979-8-4007-0466-6 |
DOIs | |
Publication status | Published - 2024 |
Event | 32nd ACM Conference on User Modeling, Adaptation and Personalization - Cagliari, Italy Duration: 1 Jul 2024 → 4 Jul 2024 Conference number: 32 https://www.um.org/umap2024/ |
Conference
Conference | 32nd ACM Conference on User Modeling, Adaptation and Personalization |
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Abbreviated title | UMAP 2024 |
Country/Territory | Italy |
City | Cagliari |
Period | 1/07/24 → 4/07/24 |
Internet address |
Keywords
- calibration
- preference dynamics
- recommender systems