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Erschienen in:
Recommender Systems: Introduction and Challenges Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

13. Music Recommender Systems

verfasst von : Markus Schedl, Peter Knees, Brian McFee, Dmitry Bogdanov, Marius Kaminskas

Erschienen in: Recommender Systems Handbook

Verlag: Springer US

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Abstract

This chapter gives an introduction to music recommender systems research. We highlight the distinctive characteristics of music, as compared to other kinds of media. We then provide a literature survey of content-based music recommendation, contextual music recommendation, hybrid methods, and sequential music recommendation, followed by overview of evaluation strategies and commonly used data sets. We conclude by pointing to the most important challenges faced by music recommendation research.

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Vorheriges Kapitel Panorama of Recommender Systems to Support Learning
Nächstes Kapitel The Anatomy of Mobile Location-Based Recommender Systems
Fußnoten
1
We will not further detail collaborative filtering of music ratings in this chapter. To understand the principles of this technique, we refer the reader to Chap. 2
 
2
To avoid confusion, we note that content has different connotations within the MIR and recommender systems communities. MIR makes an explicit distinction between (content-based) approaches that operate directly on audio signals and (metadata) approaches that derive item descriptors from external sources, e.g., web documents [70]. In recommender systems research, as in the remainder of this chapter, both types of approaches are described as “content-based”.
 
17
http://​zune.​net; now Xbox Music.
 
19
In AotM-2011, this figure refers to the sum of the length of all playlists, where length is measured as the number of songs.
 
20
For AotM-2011 this is partially the case, as not all playlist categories refer to contextual factors.
 
24
There exist many more music benchmarking activities which are oriented towards retrieval or annotation, e.g., MIREX (http://​www.​music-ir.​org/​mirex/​wiki) or MusiClef (http://​www.​cp.​jku.​at/​datasets/​musiclef).
 
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Metadaten
Titel
Music Recommender Systems
verfasst von
Markus Schedl
Peter Knees
Brian McFee
Dmitry Bogdanov
Marius Kaminskas
Copyright-Jahr
2015
Verlag
Springer US
Buch
Recommender Systems Handbook

Print ISBN: 978-1-4899-7636-9

Electronic ISBN: 978-1-4899-7637-6

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-1-4899-7637-6_13