Music Recommendation and Discovery - The Long Tail, Long Fail, and Long Play in the Digital Music Space

By Òscar Celma. 2010

446 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3a8096d475468d939d36a2ad15c2044f6f21917b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Geoffray Bonnin
2. Emilia G'omez
3. Carlos Castillo
4. Dietmar Jannach
5. Dougal Shakespeare
6. Lorenzo Porcaro

In Computer Vision and Pattern Recognition

1. Emilia G'omez
2. Carlos Castillo
3. Dietmar Jannach
4. Geoffray Bonnin

In Signal Processing

1. Emilia G'omez
2. Carlos Castillo
3. Dietmar Jannach
4. Geoffray Bonnin

Citations in the corpus

• Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols (2013, unknown): “…2,056 17,770 174,091 Number of events 1,000,209 8,979 100,480,507 19,150,868 (898,073 useritem pairs) Timespan ∼3 years ∼2 years ∼6 years ∼4.5 years (2000/04/26– 2003/02/28) (2000/12/27– 2003/01/07) (1999/11/11– 2005/12/31) (2005/02/14– 2009/06/19) Sparsity 0.0447 0.0834 0.0118 0.0052 (Celma 2010).”
• Exploring Artist Gender Bias in Music Recommendation (2020, background): “Highlighted in literature as a prominent issue for CF algorithms [1, 10, 34], Kowald et al.”
• Music Recommendation (2016, background): “Parts of our discussion will be based on [5], [8] and [22], which represent recent overviews on music recommendation and playlist generation.”