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

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2 Author(s)
Yu-Chuan Chang ; Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei ; Shyi-Ming Chen

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

Published in:

IEEE Transactions on Evolutionary Computation  (Volume:10 ,  Issue: 5 )