Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Learning User Preferences for Recommending Radio Channels in a Music Service
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Playing music is considered essential for some businesses. When entering a clothing store, a café or a gym, there is most often some music playing in the background. The employees do not have the ability to select music optimally to maximize profit. Their expertise lies within their main duties of the workplace and they should spend most of their time focusing on those duties for an efficient workflow. The problem that arises is how businesses can play suitable music with minimal effort in music selection. To solve this, a recommender system is built with the real-time machine learning algorithm, DR-TRON. It is a lightweight and dynamic algorithm that instantly improves on user interaction. By using the dynamic nature of the algorithm, a more trivial model was initially built to test for some valuable output. Afterward, a more complex model was built where there was more consideration in music channel properties. The second model recommends suitable music channels and reduces the effort of selection.

Place, publisher, year, edition, pages
2019. , p. 77
Series
UPTEC IT, ISSN 1401-5749 ; 19008
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-393278OAI: oai:DiVA.org:uu-393278DiVA, id: diva2:1352416
Educational program
Master of Science Programme in Information Technology Engineering
Supervisors
Examiners
Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2019-09-18Bibliographically approved

Open Access in DiVA

fulltext(6333 kB)12 downloads
File information
File name FULLTEXT01.pdfFile size 6333 kBChecksum SHA-512
3a25ad2cfc2d8317cb2106083e36d446eee97092c0c959e01fa2483be0c4dbe11badddf2d13b1b421c033dc0296fad5340ef8891f2eeb7cb6d72d32681923262
Type fulltextMimetype application/pdf

By organisation
Department of Information Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 12 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 25 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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