У роботі розглянуті основні види рекомендаційних систем у мережі Інтернет, засновані на методахконтентної та колаборативної фільтрації. Розглянуто способи збору даних про користувачів з вебресурсів, необхідні для формування рекомендацій. Досліджено методи побудови класифікаторів для контентної фільтрації. Також досліджено способи обчислення коефіцієнту подібності користувачів абооб’єктів у колаборативній фільтрації.
In this article research to the robustness of recommendation systems with collaborative filtering
to information attacks, which are aimed at raising or lowering the ratings of target objects in a
system. The vulnerabilities of collaborative filtering methods to information attacks, as well as the
main types of attacks on recommendation systems - profile-injection attacks are explored. Ways to
evaluate the robustness of recommendation systems to profile-injection attacks using metrics such
as rating deviation from mean agreement and hit ratio are researched. The general method of
testing the robustness of recommendation systems is described. The classification of collaborative
filtration methods and comparisons of their robustness to information attacks are presented.
Collaborative filtering model-based methods have been found to be more robust than memorybased
methods, and item-based methods more resistant to attack than user-based methods.
Methods of identifying information attacks on recommendation systems based on the
classification of user-profiles are explored. Metrics for identify both individual bot profiles in a
system and a group of bots are researched. Ways to evaluate the quality of user profile classifiers,
including calculating metrics such as precision, recall, negative predictive value, and specificity
are described. The method of increasing the robustness of recommendation systems by entering
the user reputation parameter as well as methods for obtaining the numerical value of the user
reputation parameter is considered. The results of these researches will in the future be directed
to the development of a program model of a recommendation system for testing the robustness of
various algorithms for collaborative filtering to known information attacks.
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