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Document selection methodologies for efficient and effective learning-to-rank
2009
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09
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, widely
doi:10.1145/1571941.1572022
dblp:conf/sigir/AslamKPSY09
fatcat:7eunxfursfdjfh4drowqjygcbu