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

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.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.4 Systems and Software
          Subjects: User profiles and alert services

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.4 Systems
          Subjects: Multimedia databases
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.5 Sound and Music Computing
          Subjects: Signal analysis, synthesis, and processing

General Terms:
Algorithms, Design, Performance

Keywords:
access histories, music recommendation, perceptual properties, recommendation methods, user profiles