Case-Based Sequential Ordering of Songs for Playlist Recommendation

  • Claudio Baccigalupo
  • Enric Plaza
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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  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications 7(1), 39–59 (1994)Google Scholar
  2. 2.
    Alghoniemy, M., Tewfik, A.H.: User-defined music sequence retrieval. In: Proc. ACM Multimedia, pp. 356–358 (2000)Google Scholar
  3. 3.
    Aucouturier, J.-J., Pachet, F.: Scaling up Music Playlist Generation. In: Proc. of the 3rd IEEE Intl. Conf. on Multimedia and Expo. (2002)Google Scholar
  4. 4.
    Avesani, P., Massa, P., Nori, M., Susi, A.: Collaborative radio community. In: Proc. of Adaptive Hypermedia (2002)Google Scholar
  5. 5.
    Bradley, K., Smyth, B.: Improving recommendation diversity. In: Proc. of the 12th Irish Conference on Artificial Intelligence and Cognitive Science (2001)Google Scholar
  6. 6.
    Burkhard, H.-D.: Extending some Concepts of CBR – Foundations of Case Retrieval Nets. Case-Based Reasoning Technology – From Foundations to Applications 9, 17–50 (1998)CrossRefGoogle Scholar
  7. 7.
    Hauver, D.B., French, J.C.: Flycasting: Using Collaborative Filtering to Generate a Playlist for Online Radio. In: Proc. of the Intl. Conf. on Web Delivering of Music (2001)Google Scholar
  8. 8.
    Hayes, C., Cunningham, P.: Smart radio: Building music radio on the fly. In: Expert Systems (2000)Google Scholar
  9. 9.
    Hayes, C., Massa, P., Avesani, P., Cunningham, P.: An online evaluation framework for recommender systems. In: Proc. of the RPEC Conference (2002)Google Scholar
  10. 10.
    Hayes, C., Cunningham, P.: Context-boosting collaborative recommendations. Knowledge-Based Systems 17, 131–138 (2004)CrossRefGoogle Scholar
  11. 11.
    Hofmann, T., Puzicha, J.: Statistical models for co-occurrence data. Memorandum. MIT Artificial Intelligence Laboratory (1998)Google Scholar
  12. 12.
    Logan, B.: Music recommendation from song sets. In: Proc. of the 5th ISMIR Conference (2004)Google Scholar
  13. 13.
    Manning, C., Schütze, H.: Foundations of Natural Language Processing (1999)Google Scholar
  14. 14.
    López de Mántaras, R., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M.-L., Cox, M.T., Forbus, K., Keane, M., Aamodt, A., Watson, I.: Retrieval, reuse, revision, and retention in case-based reasoning. In: Knowledge Engineering Review (in press, 2006)Google Scholar
  15. 15.
    Pachet, F., Westerman, G., Laigre, D.: Musical data mining for electronic music distribution. In: Proc. of the Intl. Conf. on Web Delivering of Music (2001)Google Scholar
  16. 16.
    Pampalk, E., Pohle, T., Widmer, G.: Dynamic Playlist Generation Based on Skipping Behaviour. In: Proc. of the 6th ISMIR Conference (2005)Google Scholar
  17. 17.
    Pauws, S., Eggen, B.: PATS: Realization and User Evaluation of an Automatic Playlist Generator. In: Proc. of the Intl. Conf. on Music Information Retrieval (2002)Google Scholar
  18. 18.
    Platt, J., Burges, C., Swenson, S., Weare, C., Zheng, A.: Learning a gaussian process prior for automatically generating music playlists. In: Advances in Neural Information Processing Systems, vol. 14, pp. 1425–1432 (2002)Google Scholar
  19. 19.
    Plaza, E., Arcos, J.-L.: Constructive adaptation. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 306–320. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  20. 20.
    Pohle, T., Pampalk, E., Widmer, G.: Generating similarity-based playlists using traveling salesman algorithms. In: Proc. of the Intl. Conf. on Digital Audio Effects (2005)Google Scholar
  21. 21.
    Ragno, R., Burges, C.J.C., Herley, C.: Inferring Similarity Between Music Objects with Application to Playlist Generation. ACM Multimedia Information Retrieval (2005)Google Scholar
  22. 22.
    Wang, K.: Discovering Patterns from Large and Dynamic Sequential Data. Journal of Intelligent Information System, 8–33 (1995)Google Scholar

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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