Automatic generation of matching functions by genetic programming for effective information retrieval
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{WeigueFan:1999:agmfGPeir,
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author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak",
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title = "Automatic generation of matching functions by genetic
programming for effective information retrieval",
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booktitle = "Proceedings of the 1999 Americas Conference on
Information Systems",
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year = "1999",
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editor = "W. David Haseman and Derek L. Nazareth",
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pages = "49--51",
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address = "Milwaukee, WI, USA",
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month = "13-15 " # aug,
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organisation = "Association for Information Systems",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://filebox.vt.edu/users/wfan/paper/Amcis_final.pdf",
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size = "3 pages",
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abstract = "With the advent of the Internet, online resources are
increasingly available. Many users choose popular
search engines to perform an online search to satisfy
their information need. However, these search engines
tend to turn up many non-relevant documents, which make
their retrieval precision very low. How to find
appropriate ranking metrics to retrieve more relevant
documents and fewer non-relevant documents for users
remains a big challenge to the information retrieval
community. In this paper, we propose a new framework
that combines the merits of genetic programming and
relevance feedback techniques to automatically generate
and refine the matching functions used for document
ranking. This approach overcomes the shortcoming of
traditional ranking algorithms using a fixed ranking
strategy. It also gives some new ideas and hints for
information retrieval professionals.",
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notes = "AMCIS99
https://commerce.mindspring.com/www.icisnet.org/proc.html
Prototype implemented in C. Fitness based on user
feedback
Duplicate entry \cite{Fan:1999:AMCIS} removed 21 Oct
2006",
- }
Genetic Programming entries for
Weiguo Fan
Michael D Gordon
Praveen Pathak
Citations