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This poster describes an alternative approach to handling the best document selection problem. Best document selection is a common problem with many real world applications, but is not a well studied problem by itself; a simple solution would be to treat it as a ranking problem and to use existing ranking algorithms to rank all documents. We could then select only the first element of the sorted list. However, because ranking models optimize for all ranks, the model may sacrifice accuracy of the top rank for the sake of overall accuracy. This is an unnecessary trade-off. We begin by first defining an appropriate objective function for the domain, then create a boosting algorithm that explicitly targets this function. Based on experiments on a benchmark retrieval data set and Digg.com news commenting data set, we find that even a simple algorithm built for this specific problem gives better results than baseline algorithms that were designed for the more complicated ranking tasks.