THE RESEARCH TO THE ROBUSTNESS OF RECOMMENDATION SYSTEMS WITH COLLABORATIVE FILTERING TO INFORMATION ATTACKS

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 th