Article

The influence limiter: provably manipulation-resistant recommender systems

Authors:

Rahul SamiAuthors Info & Claims

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

Pages 25 - 32

https://doi.org/10.1145/1297231.1297236

Published: 19 October 2007 Publication History

Abstract

An attacker can draw attention to items that don't deserve that attention by manipulating recommender systems. We describe an influence-limiting algorithm that can turn existing recommender systems into manipulation-resistant systems. Honest reporting is the optimal strategy for raters who wish to maximize their influence. If an attacker can create only a bounded number of shills, the attacker can mislead only a small amount. However, the system eventually makes full use of information from honest, informative raters. We describe both the influence limits and the information loss incurred due to those limits in terms of information-theoretic concepts of loss functions and entropies.

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Cited By

Carayol NJackson M(2024)Finding the Wise and the Wisdom in a Crowd: Estimating Underlying Qualities of Reviewers and ItemsThe Economic Journal10.1093/ej/ueae045134:663(2712-2745)Online publication date: 24-May-2024
https://doi.org/10.1093/ej/ueae045
Lev OMattei NTurrini PZhydkov S(2023)PeerNomination: A novel peer selection algorithm to handle strategic and noisy assessmentsArtificial Intelligence10.1016/j.artint.2022.103843316(103843)Online publication date: Mar-2023
https://doi.org/10.1016/j.artint.2022.103843
Malchik CFeigenbaum J(2022)Toward User Control over Information Access: A Sociotechnical ApproachProceedings of the 2022 New Security Paradigms Workshop10.1145/3584318.3584327(117-129)Online publication date: 24-Oct-2022
https://dl.acm.org/doi/10.1145/3584318.3584327
Show More Cited By

Index Terms

The influence limiter: provably manipulation-resistant recommender systems
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

October 2007

222 pages

ISBN:9781595937308

DOI:10.1145/1297231

General Chair:
Joseph A. Konstan
University of Minnesota, USA
,
Program Chairs:
John Riedl
University of Minnesota, USA
,
Barry Smyth
University College, Dublin, Ireland

Copyright © 2007 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: 19 October 2007

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RecSys07

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RecSys07: ACM Conference on Recommender Systems

October 19 - 20, 2007

MN, Minneapolis, USA

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

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Cited By

Carayol NJackson M(2024)Finding the Wise and the Wisdom in a Crowd: Estimating Underlying Qualities of Reviewers and ItemsThe Economic Journal10.1093/ej/ueae045134:663(2712-2745)Online publication date: 24-May-2024
https://doi.org/10.1093/ej/ueae045
Lev OMattei NTurrini PZhydkov S(2023)PeerNomination: A novel peer selection algorithm to handle strategic and noisy assessmentsArtificial Intelligence10.1016/j.artint.2022.103843316(103843)Online publication date: Mar-2023
https://doi.org/10.1016/j.artint.2022.103843
Malchik CFeigenbaum J(2022)Toward User Control over Information Access: A Sociotechnical ApproachProceedings of the 2022 New Security Paradigms Workshop10.1145/3584318.3584327(117-129)Online publication date: 24-Oct-2022
https://dl.acm.org/doi/10.1145/3584318.3584327
Himeur YSayed AAlsalemi ABensaali FAmira AVarlamis IEirinaki MSardianos CDimitrakopoulos G(2022)Blockchain-based recommender systemsComputer Science Review10.1016/j.cosrev.2021.10043943:COnline publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1016/j.cosrev.2021.100439
Rodrigues BFranco MKiller CScheid EStiller B(2022)On Trust, Blockchain, and Reputation SystemsHandbook on Blockchain10.1007/978-3-031-07535-3_9(299-337)Online publication date: 4-Nov-2022
https://doi.org/10.1007/978-3-031-07535-3_9
Pipergias Analytis PBarkoczi DLorenz-Spreen PHerzog S(2020)The Structure of Social Influence in Recommender NetworksProceedings of The Web Conference 202010.1145/3366423.3380020(2655-2661)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380020
Khan AYadav AChand Rahi ASinha J(2020)Systematic Review on Recommendation Systems2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)10.1109/ICACCCN51052.2020.9362888(952-956)Online publication date: 18-Dec-2020
https://doi.org/10.1109/ICACCCN51052.2020.9362888
Ghalme GGujar SKumar AJain SNarahari YAndre EKoenig SDastani MSukthankar G(2018)Design of Coalition Resistant Credit Score Functions for Online Discussion ForumsProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237404(95-103)Online publication date: 9-Jul-2018
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Li DChen CLv QGu HLu TShang LGu NChu SChampin PGandon FMédini LLalmas MIpeirotis P(2018)AdaErrorProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186155(741-751)Online publication date: 10-Apr-2018
https://dl.acm.org/doi/10.1145/3178876.3186155
Chatzopoulos DGujar SFaltings BHui P(2018)Privacy Preserving and Cost Optimal Mobile Crowdsensing Using Smart Contracts on Blockchain2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)10.1109/MASS.2018.00068(442-450)Online publication date: Oct-2018
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