The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
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The broad motivation for our research is to build manipulation resistant news recommender systems. However, there can be several different algorithms that are used to generate news recommendations, and the strategies for manipulation resistance are likely specific to the algorithm (or class of algorithm) employed. In this paper we will focus on a common method used by many media sites of recommending the N most read (or popular) articles (e.g. New York Times, BBC, Wall Street Journal all prominently use this). Through simulation results we show that whereas recommendation of the N most read articles is easily susceptible to manipulation, a simple probabilistic variant is more robust to common manipulation strategies. Further, for the "N most read" recommender, probabilistic selection has other desirable properties. Specifically, the (N+1)th article, which may have "just" missed making the cutoff, is unduly penalized under common user models. Small differences initially are easily amplified -- an observation that can be used by manipulators. Probabilistic selection, on the other hand, creates no such artificial penalty. We also use classical results from urn models to derive theoretical results for special cases.