Разработка рекомендательных систем для повышения эффективности регулируемых закупок в электроэнергетике
Аннотация
В статье рассмотрены пути повышения эффективности функционирования рынка регулируемых закупок за счет внедрения рекомендательных систем в существующую ИТ-инфраструктуру закупок. На примере государственных, муниципальных и коммерческих закупок электроэнергетических товаров рассмотрены перспективные для внедрения классы рекомендательных систем, предложена методология разработки подобных сервисов, раскрыты алгоритмы обработки, конфигурации и интерпретации данных, необходимых для их функционирования. Обосновано отличие авторского подхода к созданию сервисов от ранее опубликованных работ, проведена апробация и А/В тестирование, представлена оценка эффективности. Получены результаты, имеющие научную новизну (обоснована методология использования нейронный сетей применительно к отрасли закупок) и практическую значимость (достигнута экономия времени заказчика на поиск поставщиков до 40%, расширен пул потенциальных поставщиков, диверсифицированы риски поставщиков за счет подбора релевантных для них процедур из новых сфер и от новых заказчиков, обеспечена возможность поставщикам находить до 2–3 новых заказчиков за 1 рекомендательную рассылку с периодичностью 1–2 раза в неделю). Предложено внедрение разработок в практику оператора электронных торгов по госзакупкам. Дальнейшее развитие рекомендательных сервисов и решений для сферы закупок авторы видят в улучшении анализа семантического (текстового, логического, визуального) содержания документов закупки, а также поведенческих стратегий поставщиков. Риски и ограничения же связывают с дороговизной содержания штата разработчиков-практиков по нейронным сетям, возможными галлюцинациями нейронных сетей и их высокой чувствительностью к ошибке и качеству исходных дата-сетов.
Исследование выполнено при финансовой поддержке Российского научного фонда (проект 23-28-01644).
Скачивания
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