Using machine learning methods to improve the efficiency of the cosmetological services administration process

O.V. Zakharova, L.O. Spektorovska

Abstract


The article is devoted to solving the problem of improving the quality of cosmetic service provision during the rapid scaling of an applied system. This process is accompanied by the creation of a large number of new roles and types of services, a significant expansion of the client base and communication network. Accordingly, it also increases significantly the volume of information that requires processing. The aim of the study is to develop approaches that would increase the efficiency of administering cosmetic services through the automation of incoming message processing and their multi-criteria categorization. The criteria identified for categorization are: message type, priority, the specialist, and the type of service. The paper also includes a review of existing approaches, taking into account the formulation of the applied task. This allows to conclude: to achieve the stated objective it is advisable to use a combination of text data preprocessing, feature extraction methods, and classical machine learning models.

Problems in programming 2026; 1: 82-92


Keywords


machine learning; text classification; rule-based systems; gibrid systems; transformers; message cat egorization; routing of requests; prediction; training data

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DOI: https://doi.org/10.15407/pp2026.01.082

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