Sequence-based context-aware music recommendation

Dongjing WANG, Shuiguang DENG, Guandong XU

Research output: Contribution to journalArticlespeer-review

57 Citations (Scopus)


Contextual factors greatly affect users’ preferences for music, so they can benefit music recommendation and music retrieval. However, how to acquire and utilize the contextual information is still facing challenges. This paper proposes a novel approach for context-aware music recommendation, which infers users’ preferences for music, and then recommends music pieces that fit their real-time requirements. Specifically, the proposed approach first learns the low dimensional representations of music pieces from users’ music listening sequences using neural network models. Based on the learned representations, it then infers and models users’ general and contextual preferences for music from users’ historical listening records. Finally, music pieces in accordance with user’s preferences are recommended to the target user. Extensive experiments are conducted on real world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse data. Copyright © 2017 Springer Science+Business Media, LLC.

Original languageEnglish
Pages (from-to)230-252
JournalInformation Retrieval Journal
Early online dateOct 2017
Publication statusPublished - Jun 2018


Wang, D., Deng, S., & Xu, G. (2018). Sequence-based context-aware music recommendation. Information Retrieval Journal, 21, 230-252.


  • Recommender systems
  • Context-aware
  • Sequence-based
  • Embedding
  • Neural network


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