Theoretical foundations of the integral indicator application in hyperparametric optimization
Machine Learning
2025-08-29 v1
Abstract
The article discusses the concept of hyperparametric optimization of recommendation algorithms using an integral assessment that combines various performance indicators into a single consolidated criterion. This approach is opposed to traditional methods of setting up a single metric and allows you to achieve a balance between accuracy, ranking quality, variety of output and the resource intensity of algorithms. The theoretical significance of the research lies in the development of a universal multi-criteria optimization tool that is applicable not only in recommendation systems, but also in a wide range of machine learning and data analysis tasks.
Cite
@article{arxiv.2508.20550,
title = {Theoretical foundations of the integral indicator application in hyperparametric optimization},
author = {Roman S. Kulshin and Anatoly A. Sidorov},
journal= {arXiv preprint arXiv:2508.20550},
year = {2025}
}