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Impact of listening behavior on music recommendation

Impact of listening behavior on music recommendation
Impact of listening behavior on music recommendation
The next generation of music recommendation systems will
be increasingly intelligent and likely take into account user
behavior for more personalized recommendations. In this
work we consider user behavior when making recommendations
with features extracted from a user’s history of listening
events. We investigate the impact of listener’s behavior
by considering features such as play counts, “mainstreaminess”,
and diversity in music taste on the performance
of various music recommendation approaches. The
underlying dataset has been collected by crawling social
media (specifically Twitter) for listening events. Each user’s
listening behavior is characterized into a three dimensional
feature space consisting of play count, “mainstreaminess”
(i.e. the degree to which the observed user listens to currently
popular artists), and diversity (i.e. the diversity of
genres the observed user listens to). Drawing subsets of
the 28,000 users in our dataset, according to these three
dimensions, we evaluate whether these dimensions influence
figures of merit of various music recommendation approaches,
in particular, collaborative filtering (CF) and CF
enhanced by cultural information such as users located in
the same city or country.
1-6
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Schedl, Markus
e98fac9e-e446-4b27-9cac-815480749500
Vall, Andreu
68a9a2b4-9d6d-4c99-a85d-7846bca0431e
Hauger, David
d66ffba9-9526-4254-8288-4fccead8f1ca
Tkalcic, Marko
1489bf4a-e699-4d46-b3b8-f63794894ed7
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Schedl, Markus
e98fac9e-e446-4b27-9cac-815480749500
Vall, Andreu
68a9a2b4-9d6d-4c99-a85d-7846bca0431e
Hauger, David
d66ffba9-9526-4254-8288-4fccead8f1ca
Tkalcic, Marko
1489bf4a-e699-4d46-b3b8-f63794894ed7

Farrahi, Katayoun, Schedl, Markus, Vall, Andreu, Hauger, David and Tkalcic, Marko (2014) Impact of listening behavior on music recommendation. In International Society for Music Information Retrieval. pp. 1-6 .

Record type: Conference or Workshop Item (Paper)

Abstract

The next generation of music recommendation systems will
be increasingly intelligent and likely take into account user
behavior for more personalized recommendations. In this
work we consider user behavior when making recommendations
with features extracted from a user’s history of listening
events. We investigate the impact of listener’s behavior
by considering features such as play counts, “mainstreaminess”,
and diversity in music taste on the performance
of various music recommendation approaches. The
underlying dataset has been collected by crawling social
media (specifically Twitter) for listening events. Each user’s
listening behavior is characterized into a three dimensional
feature space consisting of play count, “mainstreaminess”
(i.e. the degree to which the observed user listens to currently
popular artists), and diversity (i.e. the diversity of
genres the observed user listens to). Drawing subsets of
the 28,000 users in our dataset, according to these three
dimensions, we evaluate whether these dimensions influence
figures of merit of various music recommendation approaches,
in particular, collaborative filtering (CF) and CF
enhanced by cultural information such as users located in
the same city or country.

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Published date: October 2014

Identifiers

Local EPrints ID: 420080
URI: https://eprints.soton.ac.uk/id/eprint/420080
PURE UUID: d2c005af-0380-4700-a573-409d68242ace

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Date deposited: 26 Apr 2018 16:30
Last modified: 13 Mar 2019 18:35

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