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hal.structure.identifier
dc.contributor.authorVie, Jill-Jênn
HAL ID: 9988
ORCID: 0000-0002-9304-2220
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorYger, Florian
HAL ID: 17768
ORCID: 0000-0002-7182-8062
hal.structure.identifierautre
dc.contributor.authorLahfa, Ryan
hal.structure.identifierautre
dc.contributor.authorClement, Basile
hal.structure.identifierautre
dc.contributor.authorCocchi, Kévin
hal.structure.identifier
dc.contributor.authorChalumeau, Thomas
hal.structure.identifierautre
dc.contributor.authorKashima, Hisashi
dc.date.accessioned2018-03-21T14:57:31Z
dc.date.available2018-03-21T14:57:31Z
dc.date.issued2017
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/17580
dc.language.isoenen
dc.subjectcollaborative filteringen
dc.subjecthumanitiesen
dc.subjectlearning (artificial intelligence)en
dc.subjectleast squares approximationsen
dc.subjectrecommender systemsen
dc.subject.ddc006.3en
dc.titleUsing Posters to Recommend Anime and Mangas in a Cold-Start Scenarioen
dc.typeCommunication / Conférence
dc.description.abstractenItem cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well. This problem can be framed as follows: how to predict whether a user will like a manga that received few ratings from the community? Content-based techniques can alleviate this issue but require extra information, that is usually expensive to gather. In this paper, we use a deep learning technique, Illustration2Vec, to easily extract tag information from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE (Blended Alternate Least Squares with Explanation), a new model for collaborative filtering, that benefits from this extra information to recommend mangas. We show, using real data from an online manga recommender system called Mangaki, that our model improves substantially the quality of recommendations, especially for less-known manga, and is able to provide an interpretation of the taste of the users.en
dc.relation.ispartoftitle14th IAPR International Conference on Document Analysis and Recognition (ICDAR 2017), 2nd International Workshop on coMics ANalysis, Processing and Understanding (MANPU 2017)en
dc.relation.ispartofeditorOgier, Jean-Marc
dc.relation.ispartofeditorGarain, Utpal
dc.relation.ispartofeditorAntonacopoulos, Apostolos
dc.relation.ispartofpublnameIEEE - Institute of Electrical and Electronics Engineersen
dc.relation.ispartofpublcityPiscataway, NJen
dc.relation.ispartofdate2018-01
dc.contributor.countryeditoruniversityotherJAPAN
dc.contributor.countryeditoruniversityotherFRANCE
dc.subject.ddclabelIntelligence artificielleen
dc.relation.ispartofisbn978-1-5386-3586-5en
dc.relation.conftitleMANPU 2017 held in conjunction with ICDAR 2017en
dc.relation.confdate2017-11
dc.relation.confcityKyotoen
dc.relation.confcountryJapanen
dc.relation.forthcomingnonen
dc.identifier.doi10.1109/ICDAR.2017.287en
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2018-03-21T13:56:57Z
hal.identifierhal-01740137*
hal.version1*
hal.update.actionupdateFiles*
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