ПРОГНОЗИРОВАНИЕ ЗАРАБОТНОЙ ПЛАТЫ ПО ОПИСАНИЯМ ВАКАНСИЙ С ИСПОЛЬЗОВАНИЕМ NLP-МОДЕЛЕЙ НА ОСНОВЕ МЕХАНИЗМА ВНИМАНИЯ

В данном исследовании представлена двойная система глубокого обучения, которая прогнозирует диапазоны заработной платы, обрабатывая описания вакансий с использованием контекстных эмбеддингов на базе BERT и интеграции структурированных метаданных. Предложенный метод использует более 124 000 объявлений о работе с LinkedIn, объединяя контекстные эмбеддинги BERT с структурированной информацией о локации, отрасли, уровне опыта и типе компенсации. Модель применяет механизм multi-head attention для выявления ключевых терминов, связанных с зарплатой, что повышает интерпретируемость модели и улучшает точность прогнозов. Объединяя семантические эмбеддинги с табличными данными, модель создает мультимодальное представление, которое используется в контролируемом обучении с ординально-осведомленной функцией потерь (ordinal-aware loss). Модель демонстрирует стабильную производительность в классификации зарплат по трем категориям, достигая F1-показателей от 0,82 до 0,84. Предложенная модель обладает отличными обобщающими способностями для различных отраслей и типов должностей, обеспечивая точные прогнозы и прозрачные процессы принятия решений для приложений по бенчмаркингу заработной платы и аналитике рекрутинга.

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