Ranking functions play a substantial role in the
performance of information retrieval (IR) systems and
search engines. Although there are many ranking
functions available in the IR literature, various
empirical evaluation studies show that ranking
functions do not perform consistently well across
different contexts (queries, collections, users).
Moreover, it is often difficult and very expensive for
human beings to design optimal ranking functions that
work well in all these contexts. In this paper, we
propose a novel ranking function discovery framework
based on Genetic Programming and show through various
experiments how this new framework helps automate the
ranking function design/discovery process.
%0 Journal Article
%1 Fan2003b
%A Fan, Weiguo
%A Gordon, Michael D.
%A Pathak, Praveen
%D 2003
%J Information Processing and Management
%K Information Ranking Text algorithms, function, genetic mining programming, retrieval;
%N 4
%P 587--602
%R doi:10.1016/j.ipm.2003.08.001
%T A generic ranking function discovery framework by
genetic programming for information retrieval
%U http://www.sciencedirect.com/science/article/B6VC8-49J8S58-2/2/158a3713b59ef9defad7d00e81707f66
%V 40
%X Ranking functions play a substantial role in the
performance of information retrieval (IR) systems and
search engines. Although there are many ranking
functions available in the IR literature, various
empirical evaluation studies show that ranking
functions do not perform consistently well across
different contexts (queries, collections, users).
Moreover, it is often difficult and very expensive for
human beings to design optimal ranking functions that
work well in all these contexts. In this paper, we
propose a novel ranking function discovery framework
based on Genetic Programming and show through various
experiments how this new framework helps automate the
ranking function design/discovery process.
@article{Fan2003b,
abstract = {Ranking functions play a substantial role in the
performance of information retrieval (IR) systems and
search engines. Although there are many ranking
functions available in the IR literature, various
empirical evaluation studies show that ranking
functions do not perform consistently well across
different contexts (queries, collections, users).
Moreover, it is often difficult and very expensive for
human beings to design optimal ranking functions that
work well in all these contexts. In this paper, we
propose a novel ranking function discovery framework
based on Genetic Programming and show through various
experiments how this new framework helps automate the
ranking function design/discovery process.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Fan, Weiguo and Gordon, Michael D. and Pathak, Praveen},
biburl = {https://www.bibsonomy.org/bibtex/28952146420dfadecb4b64b423c9f8349/brazovayeye},
doi = {doi:10.1016/j.ipm.2003.08.001},
interhash = {2fc05408af4fb104a7d2f3facc0bb4be},
intrahash = {8952146420dfadecb4b64b423c9f8349},
journal = {Information Processing and Management},
keywords = {Information Ranking Text algorithms, function, genetic mining programming, retrieval;},
number = 4,
pages = {587--602},
size = {16 pages},
timestamp = {2008-06-19T17:39:22.000+0200},
title = {A generic ranking function discovery framework by
genetic programming for information retrieval},
url = {http://www.sciencedirect.com/science/article/B6VC8-49J8S58-2/2/158a3713b59ef9defad7d00e81707f66},
volume = 40,
year = 2003
}