The New Challenges when Modeling Context through Diversity over Time in Recommender Systems
Identifieur interne : 004B60 ( Hal/Curation ); précédent : 004B59; suivant : 004B61The New Challenges when Modeling Context through Diversity over Time in Recommender Systems
Auteurs : Amaury L'Huillier [France] ; Sylvain Castagnos [France] ; Anne Boyer [France]Source :
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Abstract
The main goal of recommender systems is to help users to filter all the information available by suggesting items they may like without they had to find them by themselves. Although the rating prediction is a pretty well controlled topic, being able to make a recommendation at the right moment still remain a challenging task. To this end, most researches try to integrate contextual information (weather, mood, location of users, etc.) in the recommendation process. Even if this process increases users satisfaction, using personal information faces with users' privacy issues. In a different way, our approach is only giving credits to the evolution of diversity within the recent history of consultations, allowing us to automatically detect implicit contexts. In this paper, we will discuss the scientific challenges to be overcome to take maximum advantage of those implicit contexts in the recommendation process.
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Although the rating prediction is a pretty well controlled topic, being able to make a recommendation at the right moment still remain a challenging task. To this end, most researches try to integrate contextual information (weather, mood, location of users, etc.) in the recommendation process. Even if this process increases users satisfaction, using personal information faces with users' privacy issues. In a different way, our approach is only giving credits to the evolution of diversity within the recent history of consultations, allowing us to automatically detect implicit contexts. In this paper, we will discuss the scientific challenges to be overcome to take maximum advantage of those implicit contexts in the recommendation process.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">The New Challenges when Modeling Context through Diversity over Time in Recommender Systems</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></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2016-05-02 15:53:12</date><date type="whenModified">2016-04-26 01:05:23</date><date type="whenReleased">2016-04-25 15:26:56</date><date type="whenProduced">2016-07-13</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-01306790</idno><idno type="halUri">https://hal.inria.fr/hal-01306790</idno><idno type="halBibtex">lhuillier:hal-01306790</idno><idno type="halRefHtml">Proceedings of the 24th Conference on User Modeling, Adaptation and Personalization (UMAP 2016 Doctoral Consortium), Jul 2016, Halifax, Canada. 2016, Proceedings of the 24th Conference on User Modeling, Adaptation and Personalization (UMAP 2016 Doctoral Consortium)</idno><idno type="halRef">Proceedings of the 24th Conference on User Modeling, Adaptation and Personalization (UMAP 2016 Doctoral Consortium), Jul 2016, Halifax, Canada. 2016, Proceedings of the 24th Conference on User Modeling, Adaptation and Personalization (UMAP 2016 Doctoral Consortium)</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="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">The New Challenges when Modeling Context through Diversity over Time in Recommender Systems</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><meeting><title>Proceedings of the 24th Conference on User Modeling, Adaptation and Personalization (UMAP 2016 Doctoral Consortium)</title><date type="start">2016-07-13</date><date type="end">2016-07-17</date><settlement>Halifax</settlement><country key="CA">Canada</country></meeting><imprint><biblScope unit="serie">Proceedings of the 24th Conference on User Modeling, Adaptation and Personalization (UMAP 2016 Doctoral Consortium)</biblScope><date type="datePub">2016-07-13</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Diversity</term><term xml:lang="en"> Context</term><term xml:lang="en"> Recommendation</term><term xml:lang="en"> User Privacy</term></keywords><classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">The main goal of recommender systems is to help users to filter all the information available by suggesting items they may like without they had to find them by themselves. Although the rating prediction is a pretty well controlled topic, being able to make a recommendation at the right moment still remain a challenging task. To this end, most researches try to integrate contextual information (weather, mood, location of users, etc.) in the recommendation process. Even if this process increases users satisfaction, using personal information faces with users' privacy issues. In a different way, our approach is only giving credits to the evolution of diversity within the recent history of consultations, allowing us to automatically detect implicit contexts. In this paper, we will discuss the scientific challenges to be overcome to take maximum advantage of those implicit contexts in the recommendation process.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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