THE RESEARCH TO THE ROBUSTNESS OF RECOMMENDATION SYSTEMS WITH COLLABORATIVE FILTERING TO INFORMATION ATTACKS
Open Access
- 1 January 2019
- journal article
- Published by Borys Grinchenko Kyiv University in Cybersecurity: Education, Science, Technique
- Vol. 1 (5), 95-104
- https://doi.org/10.28925/2663-4023.2019.5.95104
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 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.Keywords
This publication has 12 references indexed in Scilit:
- Trust-based recommendation systems in Internet of Things: a systematic literature reviewHuman-centric Computing and Information Sciences, 2019
- Shilling attack detection for recommender systems based on credibility of group users and rating time seriesPLOS ONE, 2018
- Trust based recommendation systemsPublished by Association for Computing Machinery (ACM) ,2013
- Preventing shilling attacks in online recommender systemsPublished by Association for Computing Machinery (ACM) ,2005
- Shilling recommender systems for fun and profitPublished by Association for Computing Machinery (ACM) ,2004
- Promoting Recommendations: An Attack on Collaborative FilteringLecture Notes in Computer Science, 2002
- Hybrid Recommender Systems: Survey and ExperimentsUser Modelling and User-Adapted Interaction, 2002
- Item-based collaborative filtering recommendation algorithmsPublished by Association for Computing Machinery (ACM) ,2001
- Immunizing online reputation reporting systems against unfair ratings and discriminatory behaviorPublished by Association for Computing Machinery (ACM) ,2000
- GroupLensPublished by Association for Computing Machinery (ACM) ,1994