Learning to Rank

author = "Andrew Trotman",

title = "Learning to Rank",

journal = "Information Retrieval",

year = "2005",

volume = "8",

pages = "359--381",

month = jan,

keywords = "genetic algorithms, genetic programming, searching, document ranking, machine learning",

annote = "The Pennsylvania State University CiteSeerX Archives",

bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",

language = "en",

oai = "oai:CiteSeerX.psu:10.1.1.1042.5037",

rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.",

URL = "

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1042.5037",

URL = "

http://ccc.inaoep.mx/%7Evillasen/bib/Trotman-lerningRank07.pdf",

URL = "

https://rdcu.be/dR76m",

DOI = "

doi:10.1007/s10791-005-6991-7",

size = "23 pages",

abstract = "New general purpose ranking functions are discovered using genetic programming. The TREC WSJ collection was chosen as a training set. A baseline comparison function was chosen as the best of inner product, probability, cosine, and Okapi BM25. An elitist genetic algorithm with a population size 100 was run 13 times for 100 generations and the best performing algorithms chosen from these. The best learnt functions, when evaluated against the best baseline function (BM25), demonstrate some significant performance differences, with improvements in mean average precision as high as 32percent observed on one TREC collection not used in training. In no test is BM25 shown to significantly outperform the best learnt function.",