GPQ: Directly Optimizing Q-measure based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{conf/cikm/LinLZX14,

• author = "Yuan Lin and Hongfei Lin and Ping Zhang and Bo Xu",
• title = "{GPQ}: Directly Optimizing {Q}-measure based on Genetic Programming",
• booktitle = "Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014",
• publisher = "ACM",
• year = "2014",
• editor = "Jianzhong Li and Xiaoyang Sean Wang and Minos N. Garofalakis and Ian Soboroff and Torsten Suel and Min Wang",
• address = "Shanghai, China",
• month = nov # " 3-7",
• pages = "1859--1862",
• keywords = "genetic algorithms, genetic programming, information retrieval, learning to rank, q-measure",
• isbn13 = "978-1-4503-2598-1",
• bibdate = "2014-11-07",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cikm/cikm2014.html#LinLZX14",
• URL = "http://dl.acm.org/citation.cfm?id=2661829",
• DOI = "doi:10.1145/2661829.2661932",
• acmid = "2661932",
• 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.",

}

Genetic Programming entries for Yuan Lin Hongfei Lin Ping Zhang Bo Xu

Citations