# Theoretical foundations of the integral indicator application in hyperparametric optimization

## TL;DR

This paper introduces a theoretical framework for using an integral indicator in hyperparametric optimization, enabling balanced multi-criteria tuning of recommendation algorithms and other machine learning models.

## Contribution

It develops a universal theoretical foundation for a multi-criteria optimization method applicable across various machine learning tasks.

## Key findings

- Proposes an integral assessment method for hyperparameter optimization.
- Balances accuracy, ranking quality, and resource use in recommendations.
- Provides a theoretical basis for multi-criteria optimization tools.

## 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.

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Source: https://tomesphere.com/paper/2508.20550