Exploitation du skipping pour la modélisation prédictive des usages du web. Vers une meilleure prise en compte du bruit
Identifieur interne : 001404 ( Hal/Checkpoint ); précédent : 001403; suivant : 001405Exploitation du skipping pour la modélisation prédictive des usages du web. Vers une meilleure prise en compte du bruit
Auteurs : Geoffray Bonnin [France] ; Armelle Brun [France] ; Anne Boyer [France]Source :
- Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle [ 0992-499X ] ; 2012-12-17.
Descripteurs français
- mix :
Abstract
La modélisation prédictive des usages du web a connu une période intense d'inves- tigation jusque la fin des années 1990. Pourtant, deux caractéristiques du web ont rarement été prises en compte : la présence de bruit et de navigations parallèles. Dans cet article, nous proposons un nouveau modèle, le modèle SBR (Skipping-Based Recommender), qui utilise une technique appelée skipping, et qui est capable de prendre en compte ces caractéristiques de la navigation web. Dans une série d'études expérimentales, nous mettons en avant les diverses contributions que possède ce modèle, et montrons que sa qualité surpasse celle des modèles de l'état de l'art.
Url:
DOI: 10.3166/ria.26.609-642
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Hal:hal-00778498Le document en format XML
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Série RIA : Revue d'Intelligence Artificielle</title><idno type="ISSN">0992-499X</idno><imprint><date type="datePub">2012-12-17</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>fouille de données</term><term>modèles de Markov</term><term>modèles de langage</term><term>modélisation prédictive</term><term>motifs séquentiels</term><term>prédictions anytime</term><term>schéma de pondération</term><term>skipping</term><term>web usage mining</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="fr">La modélisation prédictive des usages du web a connu une période intense d'inves- tigation jusque la fin des années 1990. Pourtant, deux caractéristiques du web ont rarement été prises en compte : la présence de bruit et de navigations parallèles. Dans cet article, nous proposons un nouveau modèle, le modèle SBR (Skipping-Based Recommender), qui utilise une technique appelée skipping, et qui est capable de prendre en compte ces caractéristiques de la navigation web. Dans une série d'études expérimentales, nous mettons en avant les diverses contributions que possède ce modèle, et montrons que sa qualité surpasse celle des modèles de l'état de l'art.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="fr">Exploitation du skipping pour la modélisation prédictive des usages du web. Vers une meilleure prise en compte du bruit</title><author role="aut"><persName><forename type="first">Geoffray</forename><surname>Bonnin</surname></persName><email>geoffray.bonnin@googlemail.com</email><idno type="halauthor">207206</idno><affiliation ref="#struct-206039"></affiliation></author><author role="aut"><persName><forename type="first">Armelle</forename><surname>Brun</surname></persName><email>armelle.brun@loria.fr</email><idno type="halauthor">66694</idno><affiliation ref="#struct-206039"></affiliation></author><author role="aut"><persName><forename type="first">Anne</forename><surname>Boyer</surname></persName><email>anne.boyer@loria.fr</email><idno type="halauthor">63903</idno><affiliation ref="#struct-206039"></affiliation></author><editor role="depositor"><persName><forename>Armelle</forename><surname>Brun</surname></persName><email>armelle.brun@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2013-01-20 17:24:57</date><date type="whenModified">2015-09-22 01:11:06</date><date type="whenReleased">2013-01-20 17:24:57</date><date type="whenProduced">2012-12-17</date></edition><respStmt><resp>contributor</resp><name key="113400"><persName><forename>Armelle</forename><surname>Brun</surname></persName><email>armelle.brun@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-00778498</idno><idno type="halUri">https://hal.inria.fr/hal-00778498</idno><idno type="halBibtex">bonnin:hal-00778498</idno><idno type="halRefHtml">Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, Lavoisier, 2012, 26 (6), pp.609-642. <10.3166/ria.26.609-642></idno><idno type="halRef">Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, Lavoisier, 2012, 26 (6), pp.609-642. <10.3166/ria.26.609-642></idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno></seriesStmt><notesStmt><note type="audience" n="3">National</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="fr">Exploitation du skipping pour la modélisation prédictive des usages du web. 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Pourtant, deux caractéristiques du web ont rarement été prises en compte : la présence de bruit et de navigations parallèles. Dans cet article, nous proposons un nouveau modèle, le modèle SBR (Skipping-Based Recommender), qui utilise une technique appelée skipping, et qui est capable de prendre en compte ces caractéristiques de la navigation web. Dans une série d'études expérimentales, nous mettons en avant les diverses contributions que possède ce modèle, et montrons que sa qualité surpasse celle des modèles de l'état de l'art.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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