NDLI: The influence limiter: provably manipulation-resistant recommender systems

Content Provider	ACM Digital Library
Author	Resnick, Paul Sami, Rahul
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.
Starting Page	25
Ending Page	32
Page Count	8
File Format	PDF
ISBN	9781595937308
DOI	10.1145/1297231.1297236
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-10-19
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Manipulation-resistance Shilling Recommender system
Content Type	Text
Resource Type	Article

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The influence limiter: provably manipulation-resistant recommender systems