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Indexed by:会议论文

Date of Publication:2014-11-03

Included Journals:EI、Scopus

Page Number:1859-1862

Abstract:Ranking plays an important role in information retrieval system. In recent years, a kind of research named 'learning to rank' becomes more and more popular, which applies machine learning technology to solve ranking problems. Lots of ranking models belonged to learning to rank have been proposed, such as Regression, RankNet, and ListNet. Inspired by this, we proposed a novel learning to rank algorithm named GPQ in this paper, in which genetic programming was employed to directly optimize Q-measure evaluation metric. Experimental results on OHSUMED benchmark dataset indicated that our method GPQ could be competitive with Ranking SVM, SVMMAP and ListNet, and improve the ranking accuracies. Copyright ? 2014 ACM.