Modéliser la diversité au cours du temps pour détecter le contexte dans un service de musique en ligne
Identifieur interne : 000097 ( Main/Exploration ); précédent : 000096; suivant : 000098Modéliser 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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Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 006164
- to stream Hal, to step Curation: 006164
- to stream Hal, to step Checkpoint: 000101
- to stream Main, to step Merge: 000152
- to stream Main, to step Curation: 000097
Le document en format XML
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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><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="L Huillier, Amaury" sort="L Huillier, Amaury" uniqKey="L Huillier A" first="Amaury" last="L'Huillier">Amaury L'Huillier</name></region><name sortKey="Boyer, Anne" sort="Boyer, Anne" uniqKey="Boyer A" first="Anne" last="Boyer">Anne Boyer</name><name sortKey="Castagnos, Sylvain" sort="Castagnos, Sylvain" uniqKey="Castagnos S" first="Sylvain" last="Castagnos">Sylvain Castagnos</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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