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Education and Information Technologies

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10.02.2016

Comparative analysis of Rank Aggregation techniques for metasearch using genetic algorithm

verfasst von: Parneet Kaur, Manpreet Singh, Gurpreet Singh Josan

Erschienen in: Education and Information Technologies | Ausgabe 3/2017

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Abstract

Rank Aggregation techniques have found wide applications for metasearch along with other streams such as Sports, Voting System, Stock Markets, and Reduction in Spam. This paper presents the optimization of rank lists for web queries put by the user on different MetaSearch engines. A metaheuristic approach such as Genetic algorithm based rank aggregation technique has been proposed and implemented in MATLAB for Kendall-tau as (GKTu) and Spearman’s foot rule as (GSFD) distance measures. A comparative analysis has been carried out between ranked lists for with and without GA on the basis of simulated results. From the results it has been found that proposed GA optimized rank list (for a particular query on the basis of minimum distance) is better than the conventional methods. In addition, a word association technique i.e., AND-OR operator has been applied on each query. The results are investigated in comparison to non- logic operators for the same query.

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Titel: Comparative analysis of Rank Aggregation techniques for metasearch using genetic algorithm
verfasst von: Parneet Kaur
Manpreet Singh
Gurpreet Singh Josan
Publikationsdatum: 10.02.2016
Verlag: Springer US
Erschienen in: Education and Information Technologies / Ausgabe 3/2017
Print ISSN: 1360-2357
Elektronische ISSN: 1573-7608
DOI: https://doi.org/10.1007/s10639-016-9467-z

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