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.
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References
Chen, H.C., Chen, A.L.P.: A Music Recommendation System Based on Music and User Grouping. J. Intell. Inf. Syst. 24(2/3), 113–132 (2005)
Dunn, G., Wiersema, J., Ham, J., Aroyo, L.: Evaluating Interface Variants on Personality Acquisition for Recommender Systems. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 259–270. Springer, Heidelberg (2009)
Hu, R., Pu, P.: Acceptance issues of personality-based recommender systems. In: The 3rd ACM Conference on Recommender Systems, RecSys (2009)
Logan, B.: Music recommendation from song sets. In: Proc. ISMIR (2004)
McCrae, R., John, O.: An introduction to the five-factor model and its applications. J. Pers. 60(2), 175–215 (1992)
Mohammadi, G., Vinciarelli, A., Mortillaro, M.: The voice of personality: mapping nonverbal vocal behavior into trait attributions. In: Proc. 2nd International Workshop on Social Signal Processing (2010)
Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model. User-Adap., 1–39 (2012)
Rammstedt, B., John, O.: Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. J. Res. Pers. 41(1), 203–212 (2007)
Rentfrow, P.J., Gosling, S.D.: The do re mi’s of everyday life: the structure and personality correlates of music preference. J. Pers. Soc. Psychol. 84, 1236–1253 (2003)
Rentfrow, P.J., McDonald, J.: Preference, personality, and emotion. In: Juslin, P., Slobada, J. (eds.) Handbook of Music and Emotion: Theory, Research, Applications, pp. 669–695 (2010)
Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proc. 25th Annual International ACMSIGIR Conf. Research and Development in Information Retrieval (2002)
Srivastava, R., Jiashi, F., Roy, S., Sim, T., Yan, S.: Don’t Ask Me What I’m Like, Just Watch and Listen. In: ACM International Conference on Multimedia, ACMMM (to appear, 2012)
Tzanetakis, G., Cook, P.: Music genre classification of audio signals. IEEE T. Speech Audi. P. 10(5), 293–302 (2002)
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Srivastava, R., Roy, S., Nguyen, T.D., Yan, S. (2012). Automatic User Preference Elicitation for Music Recommendation. In: Lin, W., et al. Advances in Multimedia Information Processing – PCM 2012. PCM 2012. Lecture Notes in Computer Science, vol 7674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34778-8_57
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DOI: https://doi.org/10.1007/978-3-642-34778-8_57
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