Identification of hypermedia encyclopedic user's profile using classifiers based on dissimilarities : creating a component of an expert system for Hypergeo
Identifieur interne : 000166 ( Hal/Curation ); précédent : 000165; suivant : 000167Identification of hypermedia encyclopedic user's profile using classifiers based on dissimilarities : creating a component of an expert system for Hypergeo
Auteurs : Firas Abou Latif [France]Source :
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Abstract
This thesis is devoted to identify the profile of hypermedia user, then to adapt it according to user’s profile. This profile is found by using supervised learning algorithm like SVM. The user model is one of the essential components of adaptive hypermedia. One way to characterize this model is to associate a user to a profile. Web Usage Mining (WUM) identifies this profile from traces. However, these techniques usually operate on large mass of data. In the case when not enough data are available, we propose to use the structure and the content of the hypermedia. Hence, we used supervised kernel learning algorithms for which we have defined the measure of similarity between traces based on a “distance” between documents of the site. Our approach was validated using synthetic data and then using real data from the traces of Hypergéo users, Hypergéo is an encyclopedic website specialized in geography. Our results were compared with those obtained using a techniques of WUM(the algorithm of characteristic patterns). Finally, our proposals to identify the profiles a posteriori led usto highlight five profiles. Hypergéo users are classified according to their interests when the “semantic distance” between documents is applied.
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This profile is found by using supervised learning algorithm like SVM. The user model is one of the essential components of adaptive hypermedia. One way to characterize this model is to associate a user to a profile. Web Usage Mining (WUM) identifies this profile from traces. However, these techniques usually operate on large mass of data. In the case when not enough data are available, we propose to use the structure and the content of the hypermedia. Hence, we used supervised kernel learning algorithms for which we have defined the measure of similarity between traces based on a “distance” between documents of the site. Our approach was validated using synthetic data and then using real data from the traces of Hypergéo users, Hypergéo is an encyclopedic website specialized in geography. Our results were compared with those obtained using a techniques of WUM(the algorithm of characteristic patterns). Finally, our proposals to identify the profiles a posteriori led usto highlight five profiles. Hypergéo users are classified according to their interests when the “semantic distance” between documents is applied.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Identification of hypermedia encyclopedic user's profile using classifiers based on dissimilarities : creating a component of an expert system for Hypergeo</title><title xml:lang="fr">Identification du profil des utilisateurs d’un hypermédia encyclopédique à l’aide de classifieurs basés sur des dissimilarités : création d’un composant d’un système expert pour Hypergéo</title><author role="aut"><persName><forename type="first">Firas</forename><surname>Abou Latif</surname></persName><email></email><idno type="halauthor">644317</idno><idno type="IdRef">http://www.idref.fr/154797251</idno><affiliation ref="#struct-23832"></affiliation></author><editor role="depositor"><persName><forename>ABES</forename><surname>STAR</surname></persName><email>thelec@abes.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2011-09-21 16:14:30</date><date type="whenModified">2016-03-03 01:05:32</date><date type="whenReleased">2011-09-21 16:23:56</date><date type="whenProduced">2011-07-08</date><date type="whenEndEmbargoed">2011-09-21</date><ref type="file" target="https://tel.archives-ouvertes.fr/tel-00625439/document"><date notBefore="2011-09-21"></date></ref><ref type="file" subtype="author" n="1" target="https://tel.archives-ouvertes.fr/tel-00625439/file/TheseFirasABOULATIF1.pdf"><date notBefore="2011-09-21"></date></ref></edition><respStmt><resp>contributor</resp><name key="131274"><persName><forename>ABES</forename><surname>STAR</surname></persName><email>thelec@abes.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">tel-00625439</idno><idno type="halUri">https://tel.archives-ouvertes.fr/tel-00625439</idno><idno type="halBibtex">aboulatif:tel-00625439</idno><idno type="halRefHtml">Autre [cs.OH]. INSA de Rouen, 2011. Français. <NNT : 2011ISAM0004></idno><idno type="halRef">Autre [cs.OH]. INSA de Rouen, 2011. Français. <NNT : 2011ISAM0004></idno></publicationStmt><seriesStmt><idno type="stamp" n="STAR">STAR - Dépôt national des thèses électroniques</idno><idno type="stamp" n="UNIV-LEHAVRE">Université du Havre</idno><idno type="stamp" n="UNIV-ROUEN">Université de Rouen</idno><idno type="stamp" n="LITIS">Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes</idno><idno type="stamp" n="COMUE-NORMANDIE">Normandie Université</idno></seriesStmt><notesStmt></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Identification of hypermedia encyclopedic user's profile using classifiers based on dissimilarities : creating a component of an expert system for Hypergeo</title><title xml:lang="fr">Identification du profil des utilisateurs d’un hypermédia encyclopédique à l’aide de classifieurs basés sur des dissimilarités : création d’un composant d’un système expert pour Hypergéo</title><author role="aut"><persName><forename type="first">Firas</forename><surname>Abou Latif</surname></persName><idno type="halAuthorId">644317</idno><idno type="IdRef">http://www.idref.fr/154797251</idno><affiliation ref="#struct-23832"></affiliation></author></analytic><monogr><idno type="nnt">2011ISAM0004</idno><imprint><date type="dateDefended">2011-07-08</date></imprint><authority type="institution">INSA de Rouen</authority><authority type="school">Ecole doctorale Sciences Physiques Mathématiques et de l'Information pour l'ingénieur (Saint-Etienne-du-Rouvray, Seine-Maritime)</authority><authority type="supervisor">Jean-Pierre Pecuchet</authority><authority type="supervisor">Nicolas Delestre</authority><authority type="jury">Yolaine Bourda [Président]</authority><authority type="jury">Jean-Marc Labat [Rapporteur]</authority><authority type="jury">Mustapha Lebbah</authority></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Distance and dissimilarity</term><term xml:lang="en">Supervised and unsupervised learning</term><term xml:lang="en">Visualization</term><term xml:lang="en">Dimensionality reduction</term><term xml:lang="en">Web usage mining</term><term xml:lang="en">Adaptive hypermedia.</term><term xml:lang="fr">Fouille de données d’usage du Web</term><term xml:lang="fr">Apprentissage supervisé et non supervisé</term><term xml:lang="fr">Algorithmes de projection</term><term xml:lang="fr">Distance et dissimilarité</term><term xml:lang="fr">Hypermédia adaptatif</term></keywords><classCode scheme="halDomain" n="info.info-oh">Computer Science [cs]/Other [cs.OH]</classCode><classCode scheme="halTypology" n="THESE">Theses</classCode></textClass><abstract xml:lang="en">This thesis is devoted to identify the profile of hypermedia user, then to adapt it according to user’s profile. This profile is found by using supervised learning algorithm like SVM. The user model is one of the essential components of adaptive hypermedia. One way to characterize this model is to associate a user to a profile. Web Usage Mining (WUM) identifies this profile from traces. However, these techniques usually operate on large mass of data. In the case when not enough data are available, we propose to use the structure and the content of the hypermedia. Hence, we used supervised kernel learning algorithms for which we have defined the measure of similarity between traces based on a “distance” between documents of the site. Our approach was validated using synthetic data and then using real data from the traces of Hypergéo users, Hypergéo is an encyclopedic website specialized in geography. Our results were compared with those obtained using a techniques of WUM(the algorithm of characteristic patterns). Finally, our proposals to identify the profiles a posteriori led usto highlight five profiles. Hypergéo users are classified according to their interests when the “semantic distance” between documents is applied.</abstract><abstract xml:lang="fr">L’objectif de cette thèse est d’identifier le profil d’utilisateur d’un hypermédia afin de l’adapter. Ceprofil est déterminé en utilisant des algorithmes d’apprentissage supervisé comme le SVM.Le modèle d’utilisateur est l’un des composants essentiels des hypermédias adaptatifs. Une des façons de caractériser ce modèle est d’associer l’utilisateur à un profil. Le Web Usage Mining (WUM)identifie ce profil à l’aide des traces de navigation. Toutefois, ces techniques ne fonctionnent généralement que sur de gros volumes de données. Dans le cadre de volumes de données réduits, nous proposons d’utiliser la structure et le contenu de l’hypermédia. Pour cela, nous avons utilisé des algorithmes d’apprentissage à noyau pour lesquels nous avons défini l’élément clé qu’est la mesure de similarité entre traces basée sur une « distance » entre documents du site. Notre approche a été validée à l’aide de données synthétiques puis à l’aide de données issues des traces des utilisateurs du site Hypergéo (site webencyclopédique spécialisé dans la géographie). Nos résultats ont été comparés à ceux obtenus à l’aide d’une des techniques du WUM (l’algorithme des motifs caractéristiques). Finalement, nos propositions pour identifier les profils a posteriori ont permis de mettre en évidence cinq profils. En appliquant une« distance sémantique » entre documents, les utilisateurs d’Hypergéo ont été classés correctement selon leurs centres d’intérêt.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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