Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2026
Abstract
User ratings are often treated as comparable across users, although identical scores may reflect different experiences. We study whether ratings can be viewed as user-specific discretizations of a shared semantic continuum derived from review text. Our method maps reviews into sparse semantic features with a sparse autoencoder and learns user-specific filters for each rating level. On Amazon Electronics, the learned embeddings align along a shared low-dimensional rating axis. Users differ mainly in how they anchor and partition this continuum, while preserving its overall ordinal structure. These findings support a semantic view of calibration beyond scalar bias correction.
Keywords
Calibration, Language Models, Personalization, User Modeling
Discipline
Databases and Information Systems
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
UMAP '26: Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization, Gothenburg, Sweden, June 8-11
First Page
548
Last Page
551
ISBN
9798400723117
Identifier
10.1145/3774935.3812702
Publisher
ACM
City or Country
New York
Citation
ALVES, Rodrigo and LEDENT, Antoine.
The stars align: Modeling user rating calibration with sparse semantic review features. (2026). UMAP '26: Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization, Gothenburg, Sweden, June 8-11. 548-551.
Available at: https://ink.library.smu.edu.sg/sis_research/11135
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3774935.3812702