Modelling a career guidance system using social network data analysis
Abstract
The paper addresses the problem of automating career guidance through the analysis of digital footprints of VKontakte users. The aim of the study is to enhance the accessibility and accuracy of diagnosing professional interests by means of the "AI Proforientator" mini-application on the VK Mini Apps platform. The methodology is based on a multi-label classification approach: each of the six RIASEC professional types is represented by a separate binary output with sigmoid activation, and the model is trained using the Binary Cross-Entropy loss function. Training data included users who had completed the Holland test, along with their community subscriptions collected through the Psychological Tests app. The technical implementation follows a client–server architecture with REST API, covering data preprocessing, interaction with the ML server, and result delivery. The developed system provides a full processing cycle: extraction and validation of subscriptions, probabilistic prediction of professional personality types according to Holland’s six categories, and presentation of results to the user. The model achieved a Top-1 accuracy of 47.1% and a Top-2 accuracy of 72.3%, confirming its applicability to career guidance diagnostics. The system automates the initial career guidance process and can be employed both by individual users and organisations to support educational trajectories, optimise recruitment, and improve staff development. The novelty of the work lies in the comprehensive integration of neural network methods for multi-label classification with a client–server application embedded in a social network, ensuring a personalised and scalable approach to career guidance.
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