Репутационные системы в электронной коммерции: Сравнительный анализ и перспективы моделирования присущей им нечеткости

В наши дни электронная коммерция (ЭК) показывает беспрецедентные темпы роста во всем мире, вовлекая в эту деятельность миллионы людей на всех континентах. В то же время, ЭК создает почву для злонамеренных действий, что требует особого внимания и контроля. Одним из способов минимизации таких угроз является использование репутационных систем для отслеживания степени доверия в среде пользователей сети. Большинство существующих репутационных систем основаны на сборе отзывов относительно проведенных транзакций, и они, как правило, работают с представленными в виде чисел откликами клиентов (в частности, может использоваться привычная целочисленная шкала 0..5). В целом, понятия доверия и репутации являются примерами неопределенных (неточных) информационных данных, характерных для сферы электронной коммерции. Мы предлагаем использовать аппарат нечеткой логики для формального представления пользовательских отзывов, выражающих степень удовлетворенности результатом совершенных транзакций. В работе представлен краткий сравнительный анализ наиболее известных репутационных систем, таких как EigenTrust, HonestPeer, Absolute Trust, PowerTrust и PeerTrust. С учетом выделенных в результате анализа критериев (скорость сходимости, устойчивость (робастность), наличие гиперпараметров), проведенная серия компьютерных экспериментов позволила эмпирически выделить PeerTrust как наиболее устойчивый и масштабируемый алгоритм из числа рассмотренных. При наличии ограничений в отношении имеющихся данных, подготовлены реализации (Python 3.7) и проанализированы результаты, связанные с особенностями поведения нечетких версий алгоритма PeerTrust на основе нечетких множеств типа-1 (T1FS) и интервальных нечетких множеств второго типа (IT2FS).

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