Neural Bradley-Terry Rating: Quantifying Properties from Comparisons

Satoru Fujii

2024

Abstract

Many properties in the real world doesn’t have metrics and can’t be numerically observed, making them difficult to learn. To deal with this challenging problem, prior works have primarily focused on estimating those properties by using graded human scores as the target label in the training. Meanwhile, rating algorithms based on the Bradley-Terry model are extensively studied to evaluate the competitiveness of players based on their match history. In this paper, we introduce the Neural Bradley-Terry Rating (NBTR), a novel machine learning framework designed to quantify and evaluate properties of unknown items. Our method seamlessly integrates the Bradley-Terry model into the neural network structure. Moreover, we generalize this architecture further to asymmetric environments with unfairness, a condition more commonly encountered in real-world settings. Through experimental analysis, we demonstrate that NBTR successfully learns to quantify and estimate desired properties.

Paper Citation

in Harvard Style

Fujii S. (2024). Neural Bradley-Terry Rating: Quantifying Properties from Comparisons. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 422-429. DOI: 10.5220/0012355900003636

in Bibtex Style

@conference{icaart24,
author={Satoru Fujii},
title={Neural Bradley-Terry Rating: Quantifying Properties from Comparisons},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={422-429},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012355900003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Neural Bradley-Terry Rating: Quantifying Properties from Comparisons
SN - 978-989-758-680-4
AU - Fujii S.
PY - 2024
SP - 422
EP - 429
DO - 10.5220/0012355900003636
PB - SciTePress