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On the influence of user characteristics on music recommendation

On the influence of user characteristics on music recommendation
On the influence of user characteristics on music recommendation
We investigate a range of music recommendation algorithm combinations, score aggregation functions, normalization techniques, and late fusion techniques on approximately 200 million listening events collected through Last.fm. The overall goal is to identify superior combinations for the task of artist recommendation. Hypothesizing that user characteristics influence performance on these algorithmic combinations, we consider specific user groups determined by age, gender, country, and preferred genre. Overall, we find that the performance of music recommendation algorithms highly depends on user characteristics.
339-345
Springer
Schedl, Markus
e98fac9e-e446-4b27-9cac-815480749500
Hauger, David
c3ec00b3-6263-4fe4-87b5-9586cf9f2a43
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Tkalcic, Marko
1489bf4a-e699-4d46-b3b8-f63794894ed7
Hanbury, A.
Kazai, G.
Rauber, A.
Fuhr, N.
Schedl, Markus
e98fac9e-e446-4b27-9cac-815480749500
Hauger, David
c3ec00b3-6263-4fe4-87b5-9586cf9f2a43
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Tkalcic, Marko
1489bf4a-e699-4d46-b3b8-f63794894ed7
Hanbury, A.
Kazai, G.
Rauber, A.
Fuhr, N.

Schedl, Markus, Hauger, David, Farrahi, Katayoun and Tkalcic, Marko (2015) On the influence of user characteristics on music recommendation. Hanbury, A., Kazai, G., Rauber, A. and Fuhr, N. (eds.) In European Conference on Information Retrieval. Springer. pp. 339-345 . (doi:10.1007/978-3-319-16354-3_37).

Record type: Conference or Workshop Item (Paper)

Abstract

We investigate a range of music recommendation algorithm combinations, score aggregation functions, normalization techniques, and late fusion techniques on approximately 200 million listening events collected through Last.fm. The overall goal is to identify superior combinations for the task of artist recommendation. Hypothesizing that user characteristics influence performance on these algorithmic combinations, we consider specific user groups determined by age, gender, country, and preferred genre. Overall, we find that the performance of music recommendation algorithms highly depends on user characteristics.

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Published date: March 2015

Identifiers

Local EPrints ID: 420576
URI: https://eprints.soton.ac.uk/id/eprint/420576
PURE UUID: 9b2a9734-c06b-4d06-8e88-3ebf21890b3b

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Date deposited: 10 May 2018 16:30
Last modified: 13 Mar 2019 18:35

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Contributors

Author: Markus Schedl
Author: David Hauger
Author: Katayoun Farrahi
Author: Marko Tkalcic
Editor: A. Hanbury
Editor: G. Kazai
Editor: A. Rauber
Editor: N. Fuhr

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