Abstract
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.
This research is supported in part by a MusicStrands scholarship and by CBR-ProMusic under the project TIC2003-07776-C02-02.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications 7(1), 39–59 (1994)
Alghoniemy, M., Tewfik, A.H.: User-defined music sequence retrieval. In: Proc. ACM Multimedia, pp. 356–358 (2000)
Aucouturier, J.-J., Pachet, F.: Scaling up Music Playlist Generation. In: Proc. of the 3rd IEEE Intl. Conf. on Multimedia and Expo. (2002)
Avesani, P., Massa, P., Nori, M., Susi, A.: Collaborative radio community. In: Proc. of Adaptive Hypermedia (2002)
Bradley, K., Smyth, B.: Improving recommendation diversity. In: Proc. of the 12th Irish Conference on Artificial Intelligence and Cognitive Science (2001)
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)
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)
Hayes, C., Cunningham, P.: Smart radio: Building music radio on the fly. In: Expert Systems (2000)
Hayes, C., Massa, P., Avesani, P., Cunningham, P.: An online evaluation framework for recommender systems. In: Proc. of the RPEC Conference (2002)
Hayes, C., Cunningham, P.: Context-boosting collaborative recommendations. Knowledge-Based Systems 17, 131–138 (2004)
Hofmann, T., Puzicha, J.: Statistical models for co-occurrence data. Memorandum. MIT Artificial Intelligence Laboratory (1998)
Logan, B.: Music recommendation from song sets. In: Proc. of the 5th ISMIR Conference (2004)
Manning, C., Schütze, H.: Foundations of Natural Language Processing (1999)
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)
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)
Pampalk, E., Pohle, T., Widmer, G.: Dynamic Playlist Generation Based on Skipping Behaviour. In: Proc. of the 6th ISMIR Conference (2005)
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)
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)
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)
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)
Ragno, R., Burges, C.J.C., Herley, C.: Inferring Similarity Between Music Objects with Application to Playlist Generation. ACM Multimedia Information Retrieval (2005)
Wang, K.: Discovering Patterns from Large and Dynamic Sequential Data. Journal of Intelligent Information System, 8–33 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Baccigalupo, C., Plaza, E. (2006). Case-Based Sequential Ordering of Songs for Playlist Recommendation. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds) Advances in Case-Based Reasoning. ECCBR 2006. Lecture Notes in Computer Science(), vol 4106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11805816_22
Download citation
DOI: https://doi.org/10.1007/11805816_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36843-4
Online ISBN: 978-3-540-36846-5
eBook Packages: Computer ScienceComputer Science (R0)