Music Recommendation Based on Acoustic Features and User Access Patterns

Bo Shao, Dingding Wang, Tao Li, Mitsunori Ogihara

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


Music recommendation is receiving increasing attention as the music industry develops venues to deliver music over the Internet. The goal of music recommendation is to present users lists of songs that they are likely to enjoy. Collaborative-filtering and content-based recommendations are two widely used approaches that have been proposed for music recommendation. However, both approaches have their own disadvantages: collaborative-filtering methods need a large collection of user history data and content-based methods lack the ability of understanding the interests and preferences of users. To overcome these limitations, this paper presents a novel dynamic music similarity measurement strategy that utilizes both content features and user access patterns. The seamless integration of them significantly improves the music similarity measurement accuracy and performance. Based on this strategy, recommended songs are obtained by a means of label propagation over a graph representing music similarity. Experimental results on a real data set collected from demonstrate the effectiveness of the proposed approach.

Original languageEnglish (US)
Pages (from-to)1602-1611
Number of pages10
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number8
StatePublished - Nov 2009


  • Dynamic audio similarity
  • music recommendation
  • user access patterns

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering


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