A new query reweighting method for document retrieval based on genetic algorithms

Author

Yu-Chuan Chang ; Shyi-Ming Chen

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei

Volume

10

Issue

5

fYear

2006

Firstpage

617

Lastpage

622

Abstract

In this paper, we present a new method for query reweighting to deal with document retrieval. The proposed method uses genetic algorithms to reweight a user´s query vector, based on the user´s relevance feedback, to improve the performance of document retrieval systems. It encodes a user´s query vector into chromosomes and searches for the optimal weights of query terms for retrieving documents by genetic algorithms. After the best chromosome is found, the proposed method decodes the chromosome into the user´s query vector for dealing with document retrieval. The proposed query reweighting method can find the best weights of query terms in the user´s query vector, based on the user´s relevance feedback. It can increase the precision rate and the recall rate of the document retrieval system for dealing with document retrieval

Keywords

genetic algorithms; information retrieval systems; query processing; relevance feedback; document retrieval system; genetic algorithms; query reweighting method; query vector; relevance feedback; Adaptive systems; Biological cells; Data mining; Databases; Decoding; Feedback; Frequency; Genetic algorithms; Information retrieval; Space technology; Document retrieval; genetic algorithms; query expansion; query reweighting; user´s relevance feedback;

fLanguage

English

Journal_Title

Evolutionary Computation, IEEE Transactions on

Publisher

ieee

ISSN

1089-778X

Type

jour

DOI

10.1109/TEVC.2005.863130

Filename

1705407