Modéliser la diversité au cours du temps pour détecter le contexte dans un service de musique en ligne
Identifieur interne : 006164 ( Hal/Curation ); précédent : 006163; suivant : 006165Modéliser la diversité au cours du temps pour détecter le contexte dans un service de musique en ligne
Auteurs : Amaury L'Huillier [France] ; Sylvain Castagnos [France] ; Anne Boyer [France]Source :
- Revue des Sciences et Technologies de l'Information - Série TSI : Technique et Science Informatiques [ 0752-4072 ] ; 2016.
Descripteurs français
Abstract
Many studies have shown that taking into account the context improves the quality of recommender systems. However, traditional methods infer the context using personal data (location, date, age, etc.). In this paper, we propose to automatically detect context changes, without knowledge on users (explicit context), but based on common features of consulted items (implicit context). To do this, we propose a formal model which canestablish a correspondence between the variation of diversity over time whithin the paths of users and context changes. This model has been tested on a musical corpus of more than 200,000 tracks. To validate the relevance of our model, we sought to retrieve events from the detected changes of context: our model has recovered 88% of session ends.
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BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author></analytic><series><title level="j">Revue des Sciences et Technologies de l'Information - Série TSI : Technique et Science Informatiques</title><idno type="ISSN">0752-4072</idno><imprint><date type="datePub">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>contexte</term><term>diversité</term><term>facteurs humains</term><term>modélisation utilisateur</term><term>recommandation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Many studies have shown that taking into account the context improves the quality of recommender systems. However, traditional methods infer the context using personal data (location, date, age, etc.). In this paper, we propose to automatically detect context changes, without knowledge on users (explicit context), but based on common features of consulted items (implicit context). To do this, we propose a formal model which canestablish a correspondence between the variation of diversity over time whithin the paths of users and context changes. This model has been tested on a musical corpus of more than 200,000 tracks. To validate the relevance of our model, we sought to retrieve events from the detected changes of context: our model has recovered 88% of session ends.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="fr">Modéliser la diversité au cours du temps pour détecter le contexte dans un service de musique en ligne</title><author role="aut"><persName><forename type="first">Amaury</forename><surname>L'Huillier</surname></persName><email>amaury.lhuillier@loria.fr</email><idno type="halauthor">1091195</idno><affiliation ref="#struct-206039"></affiliation></author><author role="aut"><persName><forename type="first">Sylvain</forename><surname>Castagnos</surname></persName><email>sylvain.castagnos@loria.fr</email><idno type="halauthor">63902</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>Sylvain</forename><surname>Castagnos</surname></persName><email>sylvain.castagnos@loria.fr</email></editor><funder>Région Lorraine, Communauté Urbaine du GrandNancy</funder></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2016-05-02 15:49:51</date><date type="whenModified">2016-04-20 15:44:11</date><date type="whenReleased">2016-04-10 22:57:08</date><date type="whenProduced">2016</date></edition><respStmt><resp>contributor</resp><name key="103651"><persName><forename>Sylvain</forename><surname>Castagnos</surname></persName><email>sylvain.castagnos@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01300419</idno><idno type="halUri">https://hal.inria.fr/hal-01300419</idno><idno type="halBibtex">lhuillier:hal-01300419</idno><idno type="halRefHtml">Revue des Sciences et Technologies de l'Information - Série TSI : Technique et Science Informatiques, Lavoisier, 2016</idno><idno type="halRef">Revue des Sciences et Technologies de l'Information - Série TSI : Technique et Science Informatiques, Lavoisier, 2016</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</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">Modéliser la diversité au cours du temps pour détecter le contexte dans un service de musique en ligne</title><author role="aut"><persName><forename type="first">Amaury</forename><surname>L'Huillier</surname></persName><email>amaury.lhuillier@loria.fr</email><idno type="halAuthorId">1091195</idno><affiliation ref="#struct-206039"></affiliation></author><author role="aut"><persName><forename type="first">Sylvain</forename><surname>Castagnos</surname></persName><email>sylvain.castagnos@loria.fr</email><idno type="halAuthorId">63902</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="halAuthorId">63903</idno><affiliation ref="#struct-206039"></affiliation></author></analytic><monogr><idno type="halJournalId" status="VALID">55041</idno><idno type="issn">0752-4072</idno><idno type="eissn">2116-5920</idno><title level="j">Revue des Sciences et Technologies de l'Information - Série TSI : Technique et Science Informatiques</title><imprint><publisher>Lavoisier</publisher><date type="datePub">2016</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><keywords scheme="author"><term xml:lang="fr">modélisation utilisateur</term><term xml:lang="fr">recommandation</term><term xml:lang="fr">facteurs humains</term><term xml:lang="fr">diversité</term><term xml:lang="fr">contexte</term></keywords><classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode><classCode scheme="halTypology" n="ART">Journal articles</classCode></textClass><abstract xml:lang="en">Many studies have shown that taking into account the context improves the quality of recommender systems. However, traditional methods infer the context using personal data (location, date, age, etc.). In this paper, we propose to automatically detect context changes, without knowledge on users (explicit context), but based on common features of consulted items (implicit context). To do this, we propose a formal model which canestablish a correspondence between the variation of diversity over time whithin the paths of users and context changes. This model has been tested on a musical corpus of more than 200,000 tracks. To validate the relevance of our model, we sought to retrieve events from the detected changes of context: our model has recovered 88% of session ends.</abstract><abstract xml:lang="fr">De nombreuses études ont démontré que la prise en compte du contexte améliore la qualité des systèmes de recommandation. Cependant, les méthodes traditionnelles permettent d'inférer le contexte à l'aide de données personnelles (localisation, date, âge, etc.). Dans ce papier, nous proposons de détecter automatiquement les changements de contexte, sans connaissance sur les utilisateurs (contexte explicite), mais en fonction des caractéristiques communes aux items consultés (contexte implicite). Pour ce faire, nous proposons un modèle formel capable d'établir une correspondance entre les variations de diversité au cours du temps dans les parcours des utilisateurs et les changements de contexte. Ce modèle a été testé sur un corpus musical de plus de 200.000 écoutes. Pour valider la pertinence de notre modèle, nous avons cherché à retrouver des événements à partir des changements de contexte détectés : notre modèle a ainsi permis de retrouver 88% des fins de sessions.</abstract><particDesc><org type="consortium">Yupeek</org></particDesc></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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