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Learning-to-rank has attracted great attention in the IR community. Much thought and research has been placed on query-document feature extraction and development of sophisticated learning-to-rank algorithms. However, relatively little research has been conducted on selecting documents for learning-to-rank data sets nor on the effect of these choices on the efficiency and effectiveness of learning-to-rank algorithms. In this paper, we employ a number of document selection methodologies, widelydoi:10.1145/1571941.1572022 dblp:conf/sigir/AslamKPSY09 fatcat:7eunxfursfdjfh4drowqjygcbu