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Understanding user behavior in Spotify
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
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2013 (English)In: 2013 Proceedings - IEEE INFOCOM, 2013, 220-224 p.Conference paper (Refereed)
Abstract [en]

Spotify is a peer-assisted music streaming service that has gained worldwide popularity in the past few years. Until now, little has been published about user behavior in such services. In this paper, we study the user behavior in Spotify by analyzing a massive dataset collected between 2010 and 2011. Firstly, we investigate the system dynamics including session arrival patterns, playback arrival patterns, and daily variation of session length. Secondly, we analyze individual user behavior on both multiple and single devices. Our analysis reveals the favorite times of day for Spotify users. We also show the correlations between both the length and the downtime of successive user sessions on single devices. In particular, we conduct the first analysis of the device-switching behavior of a massive user base.

Place, publisher, year, edition, pages
2013. 220-224 p.
, Proceedings - IEEE INFOCOM, ISSN 0743-166X
Keyword [en]
Arrival patterns, Daily variations, Music streaming, Peer-assisted, System Dynamics, User behaviors, User sessions, Worldwide popularity, Behavioral research
National Category
Computer and Information Science Human Computer Interaction
URN: urn:nbn:se:kth:diva-133803DOI: 10.1109/INFCOM.2013.6566767ISI: 000326335200052ScopusID: 2-s2.0-84883126992ISBN: 978-146735946-7OAI: diva2:676555
32nd IEEE Conference on Computer Communications, IEEE INFOCOM 2013; Turin, Italy, 14-19 April 2013

QC 20131206

Available from: 2013-12-06 Created: 2013-11-11 Last updated: 2014-01-28Bibliographically approved

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Kreitz, Gunnar
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Theoretical Computer Science, TCS
Computer and Information ScienceHuman Computer Interaction

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