Author : K.V.S.N.Rama Rao 1
Date of Publication :7th March 2016
Abstract: With the growth of the World Wide Web, a large amount of music data is available on the Internet. When a user searches for a track, the existing system displays a vast amount of related data. In that displayed data, there may be irrelevant data which do not match users interest. Hence, user has to search rigorously for the track which is a time taking process. So, here we design music recommendation system using big data analytics by performing content-based, collaborative and statistic-based recommendation, which are based on the favorite degrees of the users to the music groups.
- Jiawei Han, Micheline Kamber and Jian Pei for data mining concepts and techniques, third edition
- Y.Hijikata, K. Iwahama and S. Nishida, “Content-based Music Filtering System with Editable User Profile,” in Proc. of ACM Symp. On Applied Computing, Kennesaw, Georgia, 2006, pp. 1050-1057.
- E. Perik and B. de Ruyter, P. Markopoulos and B. Eggen, “The Sensitivities of User Profile Information in Music Recommender Systems,” in Proc. of the 2nd Annual Conf. on Privacy, Security and Trust, New Brunswick, Canada, 2004, pp. 137-141.
- P. Cano, M. Koppenberger and N.Wack: “An industrial strength content-based music recommendation system,” In Proc. of 28th ACM SIGIR, pp. 673, 2005
- Yajie Hu, Mitsunori Ogihara “A music recommendation system based on human behaviour” in 12th International Society for Music Information Retrieval Conference (ISMIR 2011)
- Brian McFee, Luke Barrington, and Gert R. G. Lanckriet. Learning content similarity for music recommendation. IEEE Transactions on Audio, Speech & Language Processing, 20(8), 2012
- Aaron van den Oord, Sander Dieleman, Benjamin Schrauwen for “Deep content-based music recommendation”.
- L. Barrington, R. Oda, and G. Lanckriet, “Smarter than genius? Human evaluation of music recommender systems,” in Proc. 10th Int. Conf. Music Inf. Retrieval, 2009
- O. Celma. “Music Recommendation and Discovery in the Long Tail”. PhD thesis, Universitat Pompeu Fabra, Barcelona, 2008
- Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. The million song dataset. In Proceedings of the 11th International Conference on Music Information Retrieval (ISMIR), 2011
- Philippe Hamel and Douglas Eck. Learning features from “music audio with deep belief networks”. In