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• W1480348078 abstract "Crowdsourcing platforms are now extensively used for conducting subjective pairwise comparison studies. In this setting, a pairwise comparison dataset is typically gathered via random sampling, either emph{with} or emph{without} replacement. In this paper, we use tools from random graph theory to analyze these two random sampling methods for the HodgeRank estimator. Using the Fiedler value of the graph as a measurement for estimator stability (informativeness), we provide a new estimate of the Fiedler value for these two random graph models. In the asymptotic limit as the number of vertices tends to infinity, we prove the validity of the estimate. Based on our findings, for a small number of items to be compared, we recommend a two-stage sampling strategy where a greedy sampling method is used initially and random sampling emph{without} replacement is used in the second stage. When a large number of items is to be compared, we recommend random sampling with replacement as this is computationally inexpensive and trivially parallelizable. Experiments on synthetic and real-world datasets support our analysis." @default.
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• W1480348078 date "2016-09-01" @default.
• W1480348078 modified "2023-10-18" @default.
• W1480348078 title "Analysis of crowdsourced sampling strategies for HodgeRank with sparse random graphs" @default.
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• W1480348078 doi "https://doi.org/10.1016/j.acha.2016.03.007" @default.
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