Case-Based Sequential Ordering of Songs for Playlist Recommendation

  • Claudio Baccigalupo
  • Enric Plaza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)


We present a CBR approach to musical playlist recommendation. A good playlist is not merely a bunch of songs, but a selected collection of songs, arranged in a meaningful sequence, e.g. a good DJ creates good playlists. Our CBR approach focuses on recommending new and meaningful playlists, i.e. selecting a collection of songs that are arranged in a meaningful sequence. In the proposed approach, the Case Base is formed by a large collection of playlists, previously compiled by human listeners. The CBR system first retrieves from the Case Base the most relevant playlists, then combines them to generate a new playlist, both relevant to the input song and meaningfully ordered. Some experiments with different trade-offs between the diversity and the popularity of songs in playlists are analysed and discussed.


Collaborative Filter Relevant Pattern Popular Song Short Pattern Constructive Adaptation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Claudio Baccigalupo
    • 1
  • Enric Plaza
    • 1
  1. 1.IIIA – Artificial Intelligence Research Institute, CSIC – Spanish Council for Scientific ResearchBellaterraSpain

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