NDLI: A music recommendation system based on music data grouping and user interests

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A music recommendation system based on music data grouping and user interests

Content Provider	ACM Digital Library
Author	Chen, Hung-Chen Chen, Arbee L. P.
Abstract	With the growth of the World Wide Web, a large amount of music data is available on the Internet. In addition to searching expected music objects for users, it becomes necessary to develop a recommendation service. In this paper, we design the Music Recommendation System (MRS) to provide a personalized service of music recommendation. The music objects of MIDI format are first analyzed. For each polyphonic music object, the representative track is first determined, and then six features are extracted from this track. According to the features, the music objects are properly grouped. For users, the access histories are analyzed to derive user interests. The content-based, collaborative and statistics-based recommendation methods are proposed, which are based on the favorite degrees of the users to the music groups. A series of experiments are carried out to show that our approach is feasible.
Starting Page	231
Ending Page	238
Page Count	8
File Format	PDF
ISBN	1581134363
DOI	10.1145/502585.502625
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2001-10-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	User profiles Music recommendation Recommendation methods Access histories Perceptual properties
Content Type	Text
Resource Type	Article

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