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Matrix factorization in recommender systems: How sensitive are matrix factorization models to sparsity?
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

One of the most popular methods in recommender systems are matrix factorization (MF) models. In this paper, the sensitivity of sparsity of these models are investigated using a simulation study. Using the MovieLens dataset as a base several dense matrices are created. These dense matrices are then made sparse in two different ways to simulate different kinds of data. The accuracy of MF is then measured on each of the simulated sparse matrices. This shows that the matrix factorization models are sensitive to the degree of information available. For high levels of sparsity the MF performs badly but as the information level increases the accuracy of the models improve, for both samples.

Place, publisher, year, edition, pages
2018. , p. 26
Keywords [en]
Recommender systems, Collaborative filtering, Matrix factorization
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-352653OAI: oai:DiVA.org:uu-352653DiVA, id: diva2:1214390
Subject / course
Statistics
Educational program
Master Programme in Statistics
Supervisors
Examiners
Available from: 2018-06-19 Created: 2018-06-06 Last updated: 2018-06-19Bibliographically approved

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CiteExportLink to record
Permanent link

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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