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


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.


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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

author={Fabio Gasparetti and Claudio Biancalana and Alessandro Micarelli and Alfonso Miola and Giuseppe Sansonetti},
booktitle={Proceedings of the 8th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},

in EndNote Style

JO - Proceedings of the 8th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
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