Probabilistic Query Expansion method using recommended past user queries

A plenty of Query Expansion techniques have been proposed to solve the problems of information retrieval systems, but new challenges has been introduced for these methods of expansion of user queries because of the rapid growth of the size of the Web collection. In this paper we have focused our att...

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Bibliographic Details
Published inSecond International Conference on the Innovative Computing Technology (INTECH 2012) pp. 406 - 411
Main Authors Ghali, B. E., Qadi, A. E., Ouadou, M., Aboutajdine, D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2012
Subjects
Educational institutions
Frequency measurement
Probabilistic logic
Probabilistic Method
Query Expansion
Query logs
Query recommendation
Search engines
Vectors
Weight measurement
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Summary:A plenty of Query Expansion techniques have been proposed to solve the problems of information retrieval systems, but new challenges has been introduced for these methods of expansion of user queries because of the rapid growth of the size of the Web collection. In this paper we have focused our attention on the improvement of the precision of the user query by a Probabilistic Query Expansion Method. Our approach consists on the use of information contained in query logs, which includes past user queries and their clicked documents, for providing high-level recommendations. The experiment results shows that the precision of the short queries increases even if we add a few terms, but the same number of terms added don't affect the long queries precision as much as for the short queries.
ISBN:146732678X
9781467326780
DOI:10.1109/INTECH.2012.6457806