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.

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.