Resnick, P. and Sami, R. 2007. The influence limiter: provably manipulation-resistant recommender systems. In Proceedings of the 2007 ACM Conference on Recommender Systems (Minneapolis, MN, USA, October 19 - 20, 2007). RecSys '07. ACM, New York, NY, 25-32. DOI= http://doi.acm.org/10.1145/1297231.1297236
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.