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Preventing shilling attacks in online recommender systems

Published: 04 November 2005 Publication History

Abstract

Collaborative filtering techniques have been successfully employed in recommender systems in order to help users deal with information overload by making high quality personalized recommendations. However, such systems have been shown to be vulnerable to attacks in which malicious users with carefully chosen profiles are inserted into the system in order to push the predictions of some targeted items. In this paper we propose several metrics for analyzing rating patterns of malicious users and evaluate their potential for detecting such shilling attacks. Building upon these results, we propose and evaluate an algorithm for protecting recommender systems against shilling attacks. The algorithm can be employed for monitoring user ratings and removing shilling attacker profiles from the process of computing recommendations, thus maintaining the high quality of the recommendations.

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  • (2025)Revisiting recommender systems: an investigative surveyNeural Computing and Applications10.1007/s00521-024-10828-537:4(2145-2173)Online publication date: 4-Jan-2025
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cover image ACM Conferences
WIDM '05: Proceedings of the 7th annual ACM international workshop on Web information and data management
November 2005
96 pages
ISBN:1595931945
DOI:10.1145/1097047
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 04 November 2005

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Author Tags

  1. collaborative filtering
  2. recommender systems
  3. shilling attacks
  4. web applications

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Cited By

View all
  • (2025)Shilling Attacks and Fake Reviews Injection: Principles, Models, and DatasetsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.346500812:1(362-375)Online publication date: Feb-2025
  • (2025)User similarity-based graph convolutional neural network for shilling attack detectionApplied Intelligence10.1007/s10489-025-06254-255:5Online publication date: 17-Jan-2025
  • (2025)Revisiting recommender systems: an investigative surveyNeural Computing and Applications10.1007/s00521-024-10828-537:4(2145-2173)Online publication date: 4-Jan-2025
  • (2024)Unsupervised contaminated user profile identification against shilling attack in recommender systemIntelligent Data Analysis10.3233/IDA-23057528:6(1411-1426)Online publication date: 15-Nov-2024
  • (2024)Manipulating Recommender Systems: A Survey of Poisoning Attacks and CountermeasuresACM Computing Surveys10.1145/367732857:1(1-39)Online publication date: 7-Oct-2024
  • (2024)Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender SystemProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688120(680-689)Online publication date: 8-Oct-2024
  • (2024)PARL: Poisoning Attacks Against Reinforcement Learning-based Recommender SystemsProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3637660(1331-1344)Online publication date: 1-Jul-2024
  • (2024)Performance Analysis of Classifiers in the Detection of Injection Attacks by the Association of Graph-Based Method and Generic Detection Attributes2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN)10.1109/CICN63059.2024.10847403(695-701)Online publication date: 22-Dec-2024
  • (2024)A robust ranking method for online rating systems with spammers by interval divisionExpert Systems with Applications10.1016/j.eswa.2023.121236235(121236)Online publication date: Jan-2024
  • (2024)A recommendation attack detection approach integrating CNN with BaggingComputers and Security10.1016/j.cose.2024.104030146:COnline publication date: 1-Nov-2024
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