Zobrazit v EDS

Relevance meets calibration: Triple calibration distance design for neighbour-based recommender systems.

Uloženo v:
Podrobná bibliografie
Název: Relevance meets calibration: Triple calibration distance design for neighbour-based recommender systems.
Autoři: Chen, Zhuang, Zou, Haitao, Yu, Hualong, Zheng, Shang, Gao, Shang
Zdroj: Journal of Information Science; Feb2026, Vol. 52 Issue 1, p86-101, 16p
Témata: CALIBRATION, RECOMMENDER systems, OPTIMIZATION algorithms, EMPIRICAL research
Abstrakt: Calibrated recommendations are devoted to revealing the various preferences of users with the appropriate proportions in the recommendation list. Most of the existing calibrated-oriented recommendations take an extra postprocessing step to rerank the initial outputs. However, applying this postprocessing strategy may decrease the recommendation relevance, since the origin accurate outputs have been scattered, and they usually ignore the calibration between pairwise users/items. Instead of reranking the recommendation outputs, this article is dedicated to modifying the criterion of neighbour users' selection, where we look forward to strengthening the recommendation relevance by calibrating the neighbourhood. We propose the first-order, second-order and the third-order calibration distance based on the motivation that if a user has a similar genre distribution or genre rating schema towards the target user, then his or her suggestions will be more useful for rating prediction. We also provide an equivalent transformation for the original method to speed up the algorithm with solid theoretical proof. Experimental analysis on two publicly available data sets empirically shows that our approaches are better than some of the state-of-the-art methods in terms of recommendation relevance, calibration and efficiency. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Information Science is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
Popis
Abstrakt:Calibrated recommendations are devoted to revealing the various preferences of users with the appropriate proportions in the recommendation list. Most of the existing calibrated-oriented recommendations take an extra postprocessing step to rerank the initial outputs. However, applying this postprocessing strategy may decrease the recommendation relevance, since the origin accurate outputs have been scattered, and they usually ignore the calibration between pairwise users/items. Instead of reranking the recommendation outputs, this article is dedicated to modifying the criterion of neighbour users' selection, where we look forward to strengthening the recommendation relevance by calibrating the neighbourhood. We propose the first-order, second-order and the third-order calibration distance based on the motivation that if a user has a similar genre distribution or genre rating schema towards the target user, then his or her suggestions will be more useful for rating prediction. We also provide an equivalent transformation for the original method to speed up the algorithm with solid theoretical proof. Experimental analysis on two publicly available data sets empirically shows that our approaches are better than some of the state-of-the-art methods in terms of recommendation relevance, calibration and efficiency. [ABSTRACT FROM AUTHOR]
ISSN:01655515
DOI:10.1177/01655515231182069