Intelligence en essaim et optimisation combinatoire pour les moteurs de recherche Web

Khennak, Ilyes

Type de document Thèse
Langue eng
Titre Intelligence en essaim et optimisation combinatoire pour les moteurs de recherche Web [ressource textuelle, sauf manuscrits]
Auteur(s) Khennak, Ilyes (Auteur)
Drias, Habiba (Directeur de thèse)
Université des sciences et de la technologie Houari Boumediène (Editeur (scientifique))
Adresse bib. Alger : USTHB,2017
Collation 134 p. : ill. ; 30 cm + CD-Rom
Notes Bibliogr. p. 125-134
Notes de thèse Doctorat_LMD : Intelligence artificielle : Faculté d'Electronique et d'Informatique : Université des sciences et de la technologie Houari Boumediène : 2017
Theme Informatique
Mot (s) clé Optimisation combinatoire
Moteurs de recherche sur Internet
Algorithmes
Résumé Swarm Intelligence (SI) algorithms are now among the most widely usedsoft computing techniques for computational intelligence.Due to their simplicity and flexibility, many recent SI algorithmshave begun to receive more attention in the literature. In this work, we propose the application of SI to solve the problemof Query Expansion (QE) in Web Information Retrieval (IR). Unlike priorefforts, we introduce a novel modelling of QE that aims to find the suitable expandedquery from among a set of expanded query candidates. However, dueto the huge number of potential expanded query candidates, it is complex to produce the best one through conventional hard computing methods.Therefore, we propose to consider the problem of QE as a combinatorialoptimization problem and use SI to solve this issue.

Khennak, Ilyes
Intelligence en essaim et optimisation combinatoire pour les moteurs de recherche Web [ressource textuelle, sauf manuscrits] / Ilyes Khennak; Dir. Habiba Drias; Ed. Université des sciences et de la technologie Houari Boumediène.-Alger : USTHB,2017.-134 p. : ill. ; 30 cm + CD-Rom.
- Bibliogr. p. 125-134
Doctorat_LMD : Intelligence artificielle : Faculté d'Electronique et d'Informatique : 2017
.

Optimisation combinatoire
Moteurs de recherche sur Internet
Algorithmes

Swarm Intelligence (SI) algorithms are now among the most widely usedsoft computing techniques for computational intelligence.Due to their simplicity and flexibility, many recent SI algorithmshave begun to receive more attention in the literature. In this work, we propose the application of SI to solve the problemof Query Expansion (QE) in Web Information Retrieval (IR). Unlike priorefforts, we introduce a novel modelling of QE that aims to find the suitable expandedquery from among a set of expanded query candidates. However, dueto the huge number of potential expanded query candidates, it is complex to produce the best one through conventional hard computing methods.Therefore, we propose to consider the problem of QE as a combinatorialoptimization problem and use SI to solve this issue.

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Type de document	Thèse
Langue	eng
Titre	Intelligence en essaim et optimisation combinatoire pour les moteurs de recherche Web [ressource textuelle, sauf manuscrits]
Auteur(s)	Khennak, Ilyes (Auteur) Drias, Habiba (Directeur de thèse) Université des sciences et de la technologie Houari Boumediène (Editeur (scientifique))
Adresse bib.	Alger : USTHB,2017
Collation	134 p. : ill. ; 30 cm + CD-Rom
Notes	Bibliogr. p. 125-134
Notes de thèse	Doctorat_LMD : Intelligence artificielle : Faculté d'Electronique et d'Informatique : Université des sciences et de la technologie Houari Boumediène : 2017
Theme	Informatique
Mot (s) clé	Optimisation combinatoire Moteurs de recherche sur Internet Algorithmes
Résumé	Swarm Intelligence (SI) algorithms are now among the most widely usedsoft computing techniques for computational intelligence.Due to their simplicity and flexibility, many recent SI algorithmshave begun to receive more attention in the literature. In this work, we propose the application of SI to solve the problemof Query Expansion (QE) in Web Information Retrieval (IR). Unlike priorefforts, we introduce a novel modelling of QE that aims to find the suitable expandedquery from among a set of expanded query candidates. However, dueto the huge number of potential expanded query candidates, it is complex to produce the best one through conventional hard computing methods.Therefore, we propose to consider the problem of QE as a combinatorialoptimization problem and use SI to solve this issue.