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Identifying Patterns in User Behavior in a Music Streaming Service:: A Cluster Analysis Approach
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Logged user data has become a highly valued asset to many  Internet based services with large user bases. Being able to draw insight from this data is considered a key to gaining competitive advantages for the companies behind     the services. This study aims to identify patterns in the behavior of users when    interacting with Spotify, a music streaming service, by studying automatically     logged data. In the study, we examine several methods to perform such  analyses using machine learning techniques. We identify six different types of behavior     through k-means cluster analysis, each representing between 51.4% and 0.5% of all user sessions. We also identify five factors partly explaining the differences in behavior between different sessions. These are found    through factor analysis and account for 39% of the variance in the data. Finally, we  demonstrate how factors and clusters can be translated from numeric representations to linguistic interpretations.

Abstract [sv]

Loggad användardata har blivit en högt värderad tillgång för många Internetbaserade tjänster med en stor mängd användare. Att finna insikter från     dessa data anses vara en nyckel till att vinna konkurrensfördelar för företagen     bakom tjänsterna. Denna studie har som mål att identifiera mönster i beteendet hos användare av Spotify, en musikströmningstjänst, genom att studera loggad data. I studien utreds flera metoder för att göra denna typ av analys genom att     använda maskininlärningstekniker. Vi identifierar sex olika typer av beteende genom k-means klusteranalys, där var och en representerar beteendet i mellan    51.4 %    och    0.5 %    av    alla    sessioner. Vi    identifierar     också     fem     faktorer som förklarar en del av skillnaderna i beteende mellan användares olika     sessioner. Dessa hittas genom faktoranalys, och förklarar tillsammans 39% av variansen i studiens data. Till sist går vi igenom hur kluster och faktorer kan översättas från numeriska representationer till semantiska tolkningar.

Place, publisher, year, edition, pages
2013.
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:kth:diva-150433OAI: oai:DiVA.org:kth-150433DiVA: diva2:743384
Supervisors
Available from: 2014-12-09 Created: 2014-09-04 Last updated: 2018-01-11Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf