title: ÐÐµÑÐ¾Ð´ Ð·Ð°ÑÐ¸ÑÑ Ð¾Ñ Ð°ÑÐ°ÐºÐ¸ Ð¼Ð°Ð½Ð¸Ð¿ÑÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼ Ñ ÐºÐ¾Ð»Ð»Ð°Ð±Ð¾ÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð¹ ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸ÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸ÑÑÐ°
titleType: Subtitle
title: The method of protection against the attack of manipulation of recommendation systems with collaborative filtering
Creators: name: ÐÐ°Ð³Ð°Ð»ÑÑÐºÐ¸Ð¹, ÐÐ¼Ð¸ÑÑÐ¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ
ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ
ÐºÐ¾Ð»Ð»Ð°Ð±Ð¾ÑÐ°ÑÐ¸Ð²Ð½Ð°Ñ ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð°ÑÐ°ÐºÐ¸ ÑÐ¸Ð»Ð»Ð¸Ð½Ð³Ð°
recommendation systems
collaborative filtering
shilling attack detection

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description: The topic of the graduate qualification work is a Â«The method of protection against the attack of manipulation of recommendation systems with collaborative filteringÂ». The purpose of the study is to develop a method of protecting recommendation systems based on collaborative filtering from manipulation attacks. The subject of the research is methods that allow to protect recommendation systems with collaborative filtering from manipulation attacks. The research set the following goals: Analysis and classification of attacks aimed at manipulating the result of the work of recommendation systems with collaborative filtering. Analysis of methods of protection against attacks of manipulation of recommendation systems with collaborative filtering. Development of a method of protection against the attack of manipulation of recommendation systems with collaborative filtering. Conducting experimental testing of the developed method of protection.In the course of the work, manipulation attacks on recommendation systems with collaborative filtering were analyzed, as well as methods of protection against manipulation attacks by recommendation systems with collaborative filtering. As a result of the work, a method of protection against attacks of manipulation of recommendation systems with collaborative filtering was developed and experimental testing of the method was carried out. The results could be used as a base for designing an attack detection system for manipulating recommendation systems with collaborative filtering.