Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-34778-8_57
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dc.titleAutomatic user preference elicitation for music recommendation
dc.contributor.authorSrivastava, R.
dc.contributor.authorRoy, S.
dc.contributor.authorNguyen, T.D.
dc.contributor.authorYan, S.
dc.date.accessioned2014-06-19T03:01:11Z
dc.date.available2014-06-19T03:01:11Z
dc.date.issued2012
dc.identifier.citationSrivastava, R.,Roy, S.,Nguyen, T.D.,Yan, S. (2012). Automatic user preference elicitation for music recommendation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7674 LNCS : 605-615. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-34778-8_57" target="_blank">https://doi.org/10.1007/978-3-642-34778-8_57</a>
dc.identifier.isbn9783642347771
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/69478
dc.description.abstractRecommendation Systems involve effort from the user to elicit their preference for the item to be recommended. The contribution of this paper is in eliminating such effort by automatically assessing user's personality and using the personality scores for recommending music tracks to them. Automatic personality assessment is performed by automatically answering a personality questionnaire by observing user's audiovisual recordings. To obtain personality scores, traditionally the answers to the questionnaire are combined using a set of rules specific to the questionnaire to get personality scores. As a second contribution, an approach is proposed to automatically predict personality scores from answers to a questionnaire when the rules to combine the answers may not be known. Promising results on a dataset of 50 movie characters support the proposed approaches. © 2012 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-34778-8_57
dc.sourceScopus
dc.subjectConditional Random Fields
dc.subjectEmotion Recognition
dc.subjectMovie Analysis
dc.subjectPersonality Assessment
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/978-3-642-34778-8_57
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume7674 LNCS
dc.description.page605-615
dc.identifier.isiutNOT_IN_WOS
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