short-paper

Nuke 'Em Till They Go: Investigating Power User Attacks to Disparage Items in Collaborative Recommenders

Authors:

Carlos E. Seminario,

David C. WilsonAuthors Info & Claims

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

Pages 293 - 296

https://doi.org/10.1145/2792838.2799666

Published: 16 September 2015 Publication History

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Abstract

Recommender Systems (RSs) can be vulnerable to manipulation by malicious users who successfully bias recommendations for their own benefit or pleasure. These are known as attacks on RSs and are typically used to either promote ("push") or disparage ("nuke") targeted items contained within the recommender's user-item dataset. Our recent work with the Power User Attack (PUA) model, determined that attackers disguised as influential power users can mount successful (from the attacker's viewpoint) push attacks against user-based, item-based, and SVD-based recommenders. However, the success of push attack vectors may not be symmetric for nuke attacks, which target the opposite effect --- reducing the likelihood that target items appear in users' top-N lists. The asymmetry between push and nuke attacks is highlighted when evaluating these attacks using traditional robustness metrics such as Rank and Prediction Shift. This paper examines the PUA attack model in the context of nuke attacks, in order to investigate the differences between push and nuke attack orientations, as well as how they are evaluated. In this work we show that the PUA is able to mount successful nuke attacks against commonly-used recommender algorithms highlighting the "nuke vs. push" asymmetry in the results.

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Cited By

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Guo ZFu JSun P(2023)Reinforcement Learning Recommendation Algorithm Based on Label Value DistributionMathematics10.3390/math1113289511:13(2895)Online publication date: 28-Jun-2023
https://doi.org/10.3390/math11132895
Barbieri JAlvim LBraida FZimbrão G(2022)Simulating real profiles for shilling attacksKnowledge-Based Systems10.1016/j.knosys.2021.107390230:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.knosys.2021.107390
Ding QLiu PZhu ZDuan HXu F(2021)RMPD: Method for Enhancing the Robustness of Recommendations With Attack EnvironmentsIEEE Access10.1109/ACCESS.2021.30541229(17843-17853)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3054122
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Index Terms

Nuke 'Em Till They Go: Investigating Power User Attacks to Disparage Items in Collaborative Recommenders
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

September 2015

414 pages

ISBN:9781450336925

DOI:10.1145/2792838

General Chairs:
Hannes Werthner
TU Wien, Austria
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria
,
Program Chairs:
Jennifer Golbeck
University of Maryland, USA
,
Giovanni Semeraro
University of Bari Aldo Moro, Italy

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2015

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RecSys '15

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SIGCHI

RecSys '15: Ninth ACM Conference on Recommender Systems

September 16 - 20, 2015

Vienna, Austria

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RecSys '15 Paper Acceptance Rate 28 of 131 submissions, 21%;

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

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Cited By

View all

Guo ZFu JSun P(2023)Reinforcement Learning Recommendation Algorithm Based on Label Value DistributionMathematics10.3390/math1113289511:13(2895)Online publication date: 28-Jun-2023
https://doi.org/10.3390/math11132895
Barbieri JAlvim LBraida FZimbrão G(2022)Simulating real profiles for shilling attacksKnowledge-Based Systems10.1016/j.knosys.2021.107390230:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.knosys.2021.107390
Ding QLiu PZhu ZDuan HXu F(2021)RMPD: Method for Enhancing the Robustness of Recommendations With Attack EnvironmentsIEEE Access10.1109/ACCESS.2021.30541229(17843-17853)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3054122
Cai HZhang F(2019)BS-SC: An Unsupervised Approach for Detecting Shilling Profiles in Collaborative Recommender SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2946247(1-1)Online publication date: 2019
https://doi.org/10.1109/TKDE.2019.2946247

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When power users attack: assessing impacts in collaborative recommender systems

Attacking item-based recommender systems with power items

Accuracy and robustness impacts of power user attacks on collaborative recommender systems