Personalized music recommendation by mining social media tags

Ja Hwung Su*, Wei Yi Chang, Vincent Shin-Mu Tseng

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

16 Scopus citations


Over the past few years, the recommender system has been proposed as a critical role to help users choose the preferred product from a massive amount of data. For music recommendation, most recent recommender systems made attempts to associate music with the user's preferences primarily based on user ratings. However, this kind of recommendation mechanism encounters the problem called rating diversity that makes the prediction results unreliable. To cope with this problem, in this paper, we propose a novel music recommendation approach that utilizes social media tags instead of ratings to calculate the similarity between music pieces. Through the proposed tag-based similarity, the user preferences hidden in tags can be inferred effectively. The empirical evaluations on real social media datasets reveal that our proposed approach using social tags outperforms the existing ones using only ratings in terms of predicting the user's preferences to music.

Original languageEnglish
Pages (from-to)303-312
Number of pages10
JournalProcedia Computer Science
StatePublished - 1 Jan 2013
Event17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, KES 2013 - Kitakyushu, Japan
Duration: 9 Sep 201311 Sep 2013


  • Collaborative filtering
  • Multimedia data mining
  • Music recommendation
  • Social tags mining

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