Abstract: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 f… Show more
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