{"ID":2877069,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.20550","arxiv_id":"2508.20550","title":"Theoretical foundations of the integral indicator application in hyperparametric optimization","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.","short_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...","url_abs":"https://arxiv.org/abs/2508.20550","url_pdf":"https://arxiv.org/pdf/2508.20550v1","authors":"[\"Roman S. Kulshin\",\"Anatoly A. Sidorov\"]","published":"2025-08-28T08:41:38Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}