Article

Analysis of a low-dimensional linear model under recommendation attacks

Authors:

Fillia MakedonAuthors Info & Claims

SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 517 - 524

https://doi.org/10.1145/1148170.1148259

Published: 06 August 2006 Publication History

Abstract

Collaborative filtering techniques have become popular in the past decade as an effective way to help people deal with information overload. Recent research has identified significant vulnerabilities in collaborative filtering techniques. Shilling attacks, in which attackers introduce biased ratings to influence recommendation systems, have been shown to be effective against memory-based collaborative filtering algorithms. We examine the effectiveness of two popular shilling attacks (the random attack and the average attack) on a model-based algorithm that uses Singular Value Decomposition (SVD) to learn a low-dimensional linear model. Our results show that the SVD-based algorithm is much more resistant to shilling attacks than memory-based algorithms. Furthermore, we develop an attack detection method directly built on the SVD-based algorithm and show that this method detects random shilling attacks with high detection rates and very low false alarm rates.

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Cited By

Bansal SBaliyan N(2023)Detecting Group Shilling Profiles in Recommender Systems: A Hybrid Clustering and Grey Wolf Optimizer TechniqueDesign and Applications of Nature Inspired Optimization10.1007/978-3-031-17929-7_7(133-161)Online publication date: 3-Jan-2023
https://doi.org/10.1007/978-3-031-17929-7_7
Zhou QHuang CDuan L(2022)Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks OptimizationApplied Sciences10.3390/app12191013512:19(10135)Online publication date: 9-Oct-2022
https://doi.org/10.3390/app121910135
Alonso SBobadilla JOrtega FMoya R(2019)Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender SystemsIEEE Access10.1109/ACCESS.2019.29058627(41782-41798)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2905862
Show More Cited By

Index Terms

Analysis of a low-dimensional linear model under recommendation attacks

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Published In

cover image ACM Conferences

SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

August 2006

768 pages

ISBN:1595933697

DOI:10.1145/1148170

General Chair:
Efthimis N. Efthimiadis
University of Washington
,
Program Chairs:
Susan Dumais
Microsoft Research, Redmond
,
David Hawking
CSIRO ICT Centre, Canberra, Australia
,
Kalervo Järvelin,
University of Tampere, Finland

Copyright © 2006 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: 06 August 2006

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SIGIR06: The 29th Annual International SIGIR Conference

August 6 - 11, 2006

Washington, Seattle, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Bansal SBaliyan N(2023)Detecting Group Shilling Profiles in Recommender Systems: A Hybrid Clustering and Grey Wolf Optimizer TechniqueDesign and Applications of Nature Inspired Optimization10.1007/978-3-031-17929-7_7(133-161)Online publication date: 3-Jan-2023
https://doi.org/10.1007/978-3-031-17929-7_7
Zhou QHuang CDuan L(2022)Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks OptimizationApplied Sciences10.3390/app12191013512:19(10135)Online publication date: 9-Oct-2022
https://doi.org/10.3390/app121910135
Alonso SBobadilla JOrtega FMoya R(2019)Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender SystemsIEEE Access10.1109/ACCESS.2019.29058627(41782-41798)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2905862
Gao MLing BYang LWen JXiong QLi S(2019)From similarity perspectiveFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-6566-y13:2(231-246)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11704-017-6566-y
Zhou WWen JQu QZeng JCheng T(2018)Shilling attack detection for recommender systems based on credibility of group users and rating time seriesPLOS ONE10.1371/journal.pone.019653313:5(e0196533)Online publication date: 9-May-2018
https://doi.org/10.1371/journal.pone.0196533
Sharon Moses JDhinesh Babu L(2018)A Fuzzy Linguistic Approach-Based Non-malicious Noise Detection Algorithm for Recommendation SystemInternational Journal of Fuzzy Systems10.1007/s40815-018-0508-120:8(2368-2382)Online publication date: 9-Jul-2018
https://doi.org/10.1007/s40815-018-0508-1
Wang YQian LLi FZhang L(2018)A Comparative Study on Shilling Detection Methods for Trustworthy RecommendationsJournal of Systems Science and Systems Engineering10.1007/s11518-018-5374-827:4(458-478)Online publication date: 19-Jul-2018
https://doi.org/10.1007/s11518-018-5374-8
Si MLi Q(2018)Shilling attacks against collaborative recommender systems: a reviewArtificial Intelligence Review10.1007/s10462-018-9655-xOnline publication date: 19-Sep-2018
https://doi.org/10.1007/s10462-018-9655-x
Davoudi AChatterjee M(2017)Detection of profile injection attacks in social recommender systems using outlier analysis2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8258235(2714-2719)Online publication date: Dec-2017
https://doi.org/10.1109/BigData.2017.8258235
Zhou WWen JXiong QZeng JLiu LCai HChen T(2017)Abnormal Group User Detection in Recommender Systems Using Multi-dimension Time SeriesCollaborate Computing: Networking, Applications and Worksharing10.1007/978-3-319-59288-6_34(373-383)Online publication date: 5-Jul-2017
https://doi.org/10.1007/978-3-319-59288-6_34
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