DocumentCode :
907835
Title :
Discovery of context-specific ranking functions for effective information retrieval using genetic programming
Author :
Fan, Weiguo ; Gordon, Michael D. ; Pathak, Praveen
Author_Institution :
Dept. of Accounting & Inf. Syst., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA
Volume :
16
Issue :
4
fYear :
2004
fDate :
4/1/2004 12:00:00 AM
Firstpage :
523
Lastpage :
527
Abstract :
The Internet and corporate intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text collections are taken into account. We argue that the ranking strategy should be context specific and we propose a , new systematic method that can automatically generate ranking strategies for different contexts based on genetic programming (GP). The new method was tested on TREC data and the results are very promising.
Keywords :
data mining; genetic algorithms; information retrieval; search engines; tree data structures; Internet; TREC data; context-specific ranking function discovery; corporate intranets; fixed ranking strategy; genetic programming; information routing; intelligent contextual information retrieval; search engines; term weighting strategy; text mining; Documentation; Genetic programming; Information retrieval; Information systems; Internet; Manuals; Routing; Search engines; Testing; Text mining;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2004.1269663
Filename :
1269663
Link To Document :
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