A novel music recommender by discovering preferable perceptual-patterns from music pieces

Ja Hwung Su*, Hsin Ho Yeh, S. Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Nowadays, advanced information and communication technologies ease the access of music pieces. However, it is still hard for the users to find what she/he prefers from a huge amount of music works. To solve this problem, most music recommenders based on collaborative filtering (called CF) utilize the rating logs to predict the user's preference. Unfortunately, CF-like recommenders cannot capture the user's preference effectively due to the gap between the complicated musical contents and diverse user preferences. To reduce the gap, in this paper, we propose a novel recommender that integrates musical contents mining and collaborative filtering to achieve high-quality music recommendation. For musical contents mining, the proposed perceptual patterns derived by Two-stage clustering are adopted as a kind of musical genes to support music recommendation. For collaborative filtering, pattern-based preference prediction can imply the user's preferred music effectively. The experimental results reveal that our proposed recommender well outperforms the existing recommenders in terms of recommendation quality.

Original languageEnglish
Title of host publicationAPPLIED COMPUTING 2010 - The 25th Annual ACM Symposium on Applied Computing
Pages1924-1928
Number of pages5
DOIs
StatePublished - 23 Jul 2010
Event25th Annual ACM Symposium on Applied Computing, SAC 2010 - Sierre, Switzerland
Duration: 22 Mar 201026 Mar 2010

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference25th Annual ACM Symposium on Applied Computing, SAC 2010
CountrySwitzerland
CitySierre
Period22/03/1026/03/10

Keywords

  • collaborative filtering
  • data mining
  • music recommendation
  • perceptual pattern
  • two-stage clustering

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