# Theoretical foundations of the integral indicator application in hyperparametric optimization

arXiv: https://arxiv.org/abs/2508.20550 · HTML: https://mlsanity.com/paper/2508.20550/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 28 Aug 2025.

## Abstract (arXiv)

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.