research-article

Unsupervised retrieval of attack profiles in collaborative recommender systems

Authors:

Michael O'Mahony,

Pádraig CunninghamAuthors Info & Claims

RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems

Pages 155 - 162

https://doi.org/10.1145/1454008.1454034

Published: 23 October 2008 Publication History

Abstract

Trust, reputation and recommendation are key components of successful e-commerce systems. However, e-commerce systems are also vulnerable in this respect because there are opportunities for sellers to gain advantage through manipulation of reputation and recommendation. One such vulnerability is the use of fraudulent user profiles to boost (or damage) the ratings of items in an online recommender system. In this paper we cast this problem as a problem of detecting anomalous structure in network analysis and propose a novel mechanism for detecting this anomalous structure. We present an evaluation that shows that this approach is effective at uncovering the types of recommender systems attack described in the literature.

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Cited By

Nguyen TQuoc Viet hung NNguyen THuynh TNguyen TWeidlich MYin H(2024)Manipulating Recommender Systems: A Survey of Poisoning Attacks and CountermeasuresACM Computing Surveys10.1145/367732857:1(1-39)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3677328
Kar PRoy MDatta SKar PRoy MDatta S(2024)Learning How to Rank and Collecting User BehaviorRecommender Systems: Algorithms and their Applications10.1007/978-981-97-0538-2_5(39-54)Online publication date: 12-Jun-2024
https://doi.org/10.1007/978-981-97-0538-2_5
Dakhnovich AZagalsky DSolovey R(2023)Method for Detecting Manipulation Attacks on Recommender Systems with Collaborative FilteringAutomatic Control and Computer Sciences10.3103/S014641162308004757:8(868-874)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.3103/S0146411623080047
Show More Cited By

Index Terms

Unsupervised retrieval of attack profiles in collaborative recommender systems
1. Computing methodologies
  1. Machine learning

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Shilling recommender systems for fun and profit
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Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items ...
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Personalization collaborative filtering recommender systems (CFRSs) are the crucial components of popular E-commerce services. In practice, CFRSs are also particularly vulnerable to "shilling" attacks or "profile injection" attacks due to their ...

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Information & Contributors

Information

Published In

cover image ACM Conferences

RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems

October 2008

348 pages

ISBN:9781605580937

DOI:10.1145/1454008

General Chair:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
,
Program Chairs:
Derek Bridge
University College Cork, Ireland
,
Bamshad Mobasher
DePaul University, USA
,
Francesco Ricci
Free University Bozen-Bolzano, Italy

Copyright © 2008 ACM.

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: 23 October 2008

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RecSys08

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RecSys08: ACM Conference on Recommender Systems

October 23 - 25, 2008

Lausanne, Switzerland

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Nguyen TQuoc Viet hung NNguyen THuynh TNguyen TWeidlich MYin H(2024)Manipulating Recommender Systems: A Survey of Poisoning Attacks and CountermeasuresACM Computing Surveys10.1145/367732857:1(1-39)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3677328
Kar PRoy MDatta SKar PRoy MDatta S(2024)Learning How to Rank and Collecting User BehaviorRecommender Systems: Algorithms and their Applications10.1007/978-981-97-0538-2_5(39-54)Online publication date: 12-Jun-2024
https://doi.org/10.1007/978-981-97-0538-2_5
Dakhnovich AZagalsky DSolovey R(2023)Method for Detecting Manipulation Attacks on Recommender Systems with Collaborative FilteringAutomatic Control and Computer Sciences10.3103/S014641162308004757:8(868-874)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.3103/S0146411623080047
Zayed RIbrahim LHefny HSalman HAlMohimeed A(2023)Experimental and Theoretical Study for the Popular Shilling Attacks Detection Methods in Collaborative Recommender SystemIEEE Access10.1109/ACCESS.2023.328940411(79358-79369)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3289404
Elattar KFouad K(2022)A Survey on Recommender Systems Challenges and Solutions2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)10.1109/MIUCC55081.2022.9781739(296-301)Online publication date: 8-May-2022
https://doi.org/10.1109/MIUCC55081.2022.9781739
Aktukmak MYilmaz YUysal I(2021)Sequential Attack Detection in Recommender SystemsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2021.307629516(3285-3298)Online publication date: 2021
https://doi.org/10.1109/TIFS.2021.3076295
Zhang FWang S(2020)Detecting Group Shilling Attacks in Online Recommender Systems Based on Bisecting K-Means ClusteringIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.30138787:5(1189-1199)Online publication date: Oct-2020
https://doi.org/10.1109/TCSS.2020.3013878
Hu DXu BWang JHan LLiu J(2020)A Shilling Attack Model Based on TextCNN2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)10.1109/AUTEEE50969.2020.9315588(282-289)Online publication date: 20-Nov-2020
https://doi.org/10.1109/AUTEEE50969.2020.9315588
Sundar ALi FZou XGao TRussomanno E(2020)Understanding Shilling Attacks and Their Detection Traits: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2020.30229628(171703-171715)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3022962
Dhawan SSingh KGambhir S(2020)Analysis on Detection of Shilling Attack in Recommendation SystemMobile Radio Communications and 5G Networks10.1007/978-981-15-7130-5_8(117-128)Online publication date: 29-Sep-2020
https://doi.org/10.1007/978-981-15-7130-5_8
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