Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Towards a New Evolutionary Subsampling Technique for Heuristic Optimisation of Load Disaggregators Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

A Music Recommendation System Based on Acoustic Features and User Personalities

verfasst von : Rui Cheng, Boyang Tang

Erschienen in: Trends and Applications in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Music recommendation attracts great attention for music providers to improve their services as the volume of new music increases quickly. It is a great challenge for users to find their interested songs from such a large size of collections. In the previous studies, common strategies can be categorized into content-based music recommendation and collaborative music filtering. Content-based recommendation systems predict users’ preferences in terms of the music content. Collaborative filtering systems predict users’ ratings based on the preferences of the friends of the targeting user. In this study, we proposed a hybrid approach to provide personalized music recommendations. This is achieved by extracting audio features of songs and integrating these features and user personalities for context-aware recommendation using the state-of-the-art support vector machines (SVM). Our experiments show the effectiveness of this proposed approach for personalized music recommendation.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Clinical Decision Support for Stroke Using Multi–view Learning Based Models for NIHSS Scores
Nächstes Kapitel A Social Spam Detection Framework via Semi-supervised Learning
Fußnoten
Literatur
1.
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, US (2011)CrossRef
2.
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRef
3.
Arnett, J.: The soundtrack of recklessness: musical preferences and reckless behavior among adolescents. J. Adolesc. Res. 7, 313–331 (1992)CrossRef
4.
Breese, J.S., Heckerman, D., Kardie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th UAI, pp. 43–52 (1998)
5.
Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998)CrossRef
6.
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)CrossRefMATH
7.
Cattell, R.B., Anderson, J.C.: The measurement of personality and behavior disorders by the I.P.A.T. music preference test. J. Appl. Psychol. 37, 446–454 (1953)CrossRef
8.
Cattell, R.B., Saunders, D.R.: Musical preferences and personality diagnosis: a factorization of one hundred and twenty themes. J. Soc. Psychol. 39, 3–24 (1954)CrossRef
9.
Cheung, K.W., Kwok, J.T., Law, M.H., Tsui, K.C.: Mining customer product ratings for personalized marketing. Decis. Support Syst. 35(2), 231–243 (2003)CrossRef
10.
Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)CrossRef
11.
Celma Herrada, Ò.: Music recommendation and discovery in the long tail (2009)
12.
Lartillot, O., Toiviainen, P.: A Matlab toolbox for musical feature extraction from audio. In: International Conference on Digital Audio Effects, pp. 237–244, September 2007
13.
Li, Q., Myaeng, S.H., Kim, B.M.: A probabilistic music recommender considering user opinions and audio features. Inf. Process. Manag. 43(2), 473–487 (2007)CrossRef
14.
Little, P., Zuckerman, M.: Sensation seeking and music preferences. Pers. Individ. Differ. 7, 575–577 (1986)CrossRef
15.
Mandel, M.I., Ellis, D.P.: Song-level features and support vector machines for music classification. In: ISMIR 2005: 6th International Conference on Music Information Retrieval: Proceedings: Variation 2: Queen Mary, University of London & Goldsmiths College, University of London, 11–15 September 2005, pp. 594–599. Queen Mary, University of London (2005)
16.
McCown, W., Keiser, R., Mulhearn, S., Williamson, D.: The role of personality and gender in preferences for exaggerated bass in music. Pers. Individ. Differ. 23, 543–547 (1997)CrossRef
17.
Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: Proceedings of 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 130–136. IEEE, June 1997
18.
Rentfrow, P.J., Gosling, S.D.: The do re mi’s of everyday life: the structure and personality correlates of music preferences. J. Pers. Soc. Psychol. 84(6), 1236 (2003)CrossRef
19.
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM, April 2001
20.
Schölkopf, B., Sung, K.K., Burges, C.J., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V.: Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Sig. Process. 45(11), 2758–2765 (1997)CrossRef
21.
Slaney, M., Weinberger, K., White, W.: Learning a metric for music similarity. In: International Symposium on Music Information Retrieval (ISMIR), September 2008
22.
Tzanetakis, G., Cook, P.: Music genre classification of audio signals. IEEE Trans. Speech Audio Process. 10(5), 293–302 (2002)CrossRef
23.
Van den Oord, A., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: Advances in Neural Information Processing Systems, pp. 2643–2651 (2003)
24.
Wang, J., De Vries, A.P., Reinders, M. J.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 501–508. ACM, August 2006
25.
Yoshii, K., Goto, M., Komatani, K., Ogata, T., Okuno, H.G.: Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In: ISMIR, vol. 6, 7th (2006)
Metadaten
Titel
A Music Recommendation System Based on Acoustic Features and User Personalities
verfasst von
Rui Cheng
Boyang Tang
Copyright-Jahr
2016
Buch
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-319-42995-3

Electronic ISBN: 978-3-319-42996-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-42996-0_17