article

Query Recommendation for Improving Search Engine Results

Authors:

Hamada M. Zahera,

Gamal F. El-Hady,

W. F. Abd El-WahedAuthors Info & Claims

International Journal of Information Retrieval Research, Volume 1, Issue 1

Pages 45 - 52

https://doi.org/10.4018/ijirr.2011010104

Published: 01 January 2011 Publication History

Abstract

As web contents grow, the importance of search engines become more critical and at the same time user satisfaction decreases. Query recommendation is a new approach to improve search results in web. In this paper a method is proposed that, given a query submitted to a search engine, suggests a list of queries that are related to the user input query. The related queries are based on previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process. The proposed method is based on clustering processes in which groups of semantically similar queries are detected. The clustering process uses the content of historical preferences of users registered in the query log of the search engine. This facility provides queries that are related to the ones submitted by users in order to direct them toward their required information. This method not only discovers the related queries but also ranks them according to a similarity measure. The method has been evaluated using real data sets from the search engine query log.

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Cited By

Li ZLiu K(2019)Recommendations for Personalized Keywords Combined with Word Vectors Clustering and Heat SortingProceedings of the 2nd International Conference on Big Data Technologies10.1145/3358528.3358575(131-135)Online publication date: 28-Aug-2019
https://dl.acm.org/doi/10.1145/3358528.3358575
Omri M(2018)Effects of Terms Recognition Mistakes on Requests Processing for Interactive Information RetrievalInternational Journal of Information Retrieval Research10.4018/ijirr.20120701022:3(19-35)Online publication date: 19-Dec-2018
https://dl.acm.org/doi/10.4018/ijirr.2012070102
El Ghali BEl Qadi A(2017)Context-aware query expansion method using Language Models and Latent Semantic AnalysesKnowledge and Information Systems10.1007/s10115-016-0952-x50:3(751-762)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s10115-016-0952-x

Index Terms

Query Recommendation for Improving Search Engine Results
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

Index terms have been assigned to the content through auto-classification.

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cover image International Journal of Information Retrieval Research

International Journal of Information Retrieval Research Volume 1, Issue 1

January 2011

76 pages

ISSN:2155-6377

EISSN:2155-6385

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IGI Global

United States

Publication History

Published: 01 January 2011

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Cited By

Li ZLiu K(2019)Recommendations for Personalized Keywords Combined with Word Vectors Clustering and Heat SortingProceedings of the 2nd International Conference on Big Data Technologies10.1145/3358528.3358575(131-135)Online publication date: 28-Aug-2019
https://dl.acm.org/doi/10.1145/3358528.3358575
Omri M(2018)Effects of Terms Recognition Mistakes on Requests Processing for Interactive Information RetrievalInternational Journal of Information Retrieval Research10.4018/ijirr.20120701022:3(19-35)Online publication date: 19-Dec-2018
https://dl.acm.org/doi/10.4018/ijirr.2012070102
El Ghali BEl Qadi A(2017)Context-aware query expansion method using Language Models and Latent Semantic AnalysesKnowledge and Information Systems10.1007/s10115-016-0952-x50:3(751-762)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s10115-016-0952-x

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