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Matrix Factorization Methods for Recommender Systems
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2013 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This thesis is a comprehensive study of matrix factorization methods used in recommender systems. We study and analyze the existing models, specifically probabilistic models used in conjunction with matrix factorization methods, for recommender systems from a machine learning perspective. We implement two different methods suggested in scientific literature and conduct experiments on the prediction accuracy of the models on the Yahoo! Movies rating dataset.

Place, publisher, year, edition, pages
2013.
Series
UMNAD, 941
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-74181OAI: oai:DiVA.org:umu-74181DiVA: diva2:633561
Uppsok
Technology
Supervisors
Examiners
Available from: 2013-06-27 Created: 2013-06-27 Last updated: 2013-06-27Bibliographically approved

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fulltext(247 kB)6822 downloads
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File name FULLTEXT01.pdfFile size 247 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
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More languages
Output format
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