WAVELET-BASED MUSIC RECOMMENDATION

Fabio Gasparetti, Claudio Biancalana, Alessandro Micarelli, Alfonso Miola, Giuseppe Sansonetti

Abstract

Recommender Systems provide suggestions for items (e.g., movies or songs) to be of use to a user. They must take into account information to deliver more useful (perceived) recommendations. Current music recommender takes an initial input of a song and plays music with similar characteristics, or music that other users have listened to along with the input song. Listening behaviors in terms of temporal information associated to ratings or playbacks are usually ignored. We propose a recommender that predicts the most rated songs that a given user is likely to play in the future analyzing and comparing user listening habits by means of signal processing techniques.

References

  1. Acampora, G., Gaeta, M., and Loia, V. (2010a). Exploring e-learning knowledge through ontological memetic agents. Comp. Intell. Mag., 5:66-77.
  2. Acampora, G., Gaeta, M., Loia, V., and Vasilakos, A. V. (2010b). Interoperable and adaptive fuzzy services for ambient intelligence applications. ACM Trans. Auton. Adapt. Syst., 5:8:1-8:26.
  3. Biancalana, C., Gasparetti, F., Micarelli, A., Miola, A., and Sansonetti, G. (2011b). Context-aware movie recommendation based on signal processing and machine learning. In Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, CAMRa 7811, pages 5-10, New York, NY, USA. ACM.
  4. Breese, J. S., Heckerman, D., and Kadie, C. M. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Cooper, G. F. and Moral, S., editors, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pages 43-52.
  5. Celma, . (2010). Music Recommendation and Discovery - The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer.
  6. Gabor, D. (1946). Theory of communication. J. Inst. Elect. Eng., 93:429-457.
  7. Gaeta, A., Gaeta, M., and Ritrovato, P. (2009). A grid based software architecture for delivery of adaptive and personalised learning experiences. Personal Ubiquitous Comput., 13:207-217.
  8. Gasparetti, F. and Micarelli, A. (2007). Personalized search based on a memory retrieval theory. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI): Special Issue on Personalization Techniques for Recommender Systems and Intelligent User Interfaces, 21(2):207-224.
  9. Groh, G. and Ehmig, C. (2007). Recommendations in taste related domains: collaborative filtering vs. social filtering. In GROUP 7807: Proceedings of the 2007 international ACM conference on Supporting group work, pages 127-136, New York, NY, USA. ACM.
  10. Iaquinta, L., de Gemmis, M., Lops, P., Semeraro, G., Filannino, M., and Molino, P. (2008). Introducing serendipity in a content-based recommender system. In Xhafa, F., Herrera, F., Abraham, A., Köppen, M., and Benítez, J. M., editors, International Conference on Hybrid Intelligent Systems (HIS 2008, pages 168- 173. IEEE Computer Society.
  11. Micarelli, A., Gasparetti, F., and Biancalana, C. (2006). Intelligent search on the internet. In Stock, O. and Schaerf, M., editors, Reasoning, Action and Interaction in AI Theories and Systems, volume 4155 of Lecture Notes in Computer Science, pages 247-264. Springer.
  12. Micarelli, A., Sciarrone, F., and Gasparetti, F. (2009). A case-based approach to adaptive hypermedia navigation. IJWLTT, 4(1):35-53.
  13. Musto, C., Semeraro, G., Lops, P., and de Gemmis, M. (2011). Random indexing and negative user preferences for enhancing content-based recommender systems. In Huemer, C. and Setzer, T., editors, ECommerce and Web Technologies, volume 85 of Lecture Notes in Business Information Processing, pages 270-281. Springer.
  14. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. (1994). Grouplens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, CSCW 7894, pages 175-186, New York, NY, USA. ACM.
  15. Schafer, J. B., Frankowski, D., Herlocker, J., and Sen, S. (2007). Collaborative filtering recommender systems. In Brusilovsky, P., Kobsa, A., and Nejdl, W., editors, The Adaptive Web: Methods and Strategies of Web Personalization, volume 4321 of Lecture Notes in Computer Science, pages 291-324. Springer Berlin, Heidelberg, Berlin, Heidelberg, and New York.
  16. Semeraro, G., Lops, P., Basile, P., and de Gemmis, M. (2009). Knowledge infusion into content-based recommender systems. In Bergman, L. D., Tuzhilin, A., Burke, R. D., Felfernig, A., and Schmidt-Thieme, L., editors, Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009, pages 301-304. ACM.
  17. Shardanand, U. and Maes, P. (1995). Social information filtering: algorithms for automating word of mouth. In Proceedings of the SIGCHI conference on Human factors in computing systems, CHI 7895, pages 210-217, New York, NY, USA. ACM Press/Addison-Wesley Publishing Co.
Download


Paper Citation


in Harvard Style

Gasparetti F., Biancalana C., Micarelli A., Miola A. and Sansonetti G. (2012). WAVELET-BASED MUSIC RECOMMENDATION . In Proceedings of the 8th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8565-08-2, pages 399-402. DOI: 10.5220/0003940903990402


in Bibtex Style

@conference{webist12,
author={Fabio Gasparetti and Claudio Biancalana and Alessandro Micarelli and Alfonso Miola and Giuseppe Sansonetti},
title={WAVELET-BASED MUSIC RECOMMENDATION},
booktitle={Proceedings of the 8th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2012},
pages={399-402},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003940903990402},
isbn={978-989-8565-08-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - WAVELET-BASED MUSIC RECOMMENDATION
SN - 978-989-8565-08-2
AU - Gasparetti F.
AU - Biancalana C.
AU - Micarelli A.
AU - Miola A.
AU - Sansonetti G.
PY - 2012
SP - 399
EP - 402
DO - 10.5220/0003940903990402