Please use this identifier to cite or link to this item:
|Title:||Automatic user preference elicitation for music recommendation|
|Keywords:||Conditional Random Fields|
|Citation:||Srivastava, 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. https://doi.org/10.1007/978-3-642-34778-8_57|
|Abstract:||Recommendation 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.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 13, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.