Change search
CiteExportLink to record
Permanent link

Direct link
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
A comparative study of regularised SVD and item-based kNN for movie recommender systems
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This thesis compares the performance of two algorithms for rating predictions in movie recommender systems. The two algorithms examined, regularised singular value decomposition (RegSVD) and item-based k-Nearest Neighbour (item-based kNN), are compared on 9 different datasets. These datasets consists of constellations of 1,000-100,000 users and 100-10,000 movies. The problem statement is to find which algorithm performs the best on each dataset with respect to both accuracy and speed. These results are then  compared in order to identify general tendencies. The experiments are performed using the implementations of the algorithms in the LibRec library, where accuracy is measured using root-mean-square error (RMSE). Finally, the results show that item-based kNN outperforms RegSVD with respect to accuracy when evaluated on smaller datasets. However, RegSVD is a better alternative for larger datasets with respect to both accuracy and execution time.

Place, publisher, year, edition, pages
2016.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-186374OAI: oai:DiVA.org:kth-186374DiVA: diva2:927078
Supervisors
Examiners
Available from: 2016-05-18 Created: 2016-05-10 Last updated: 2016-05-18Bibliographically approved

Open Access in DiVA

fulltext(868 kB)78 downloads
File information
File name FULLTEXT01.pdfFile size 868 kBChecksum SHA-512
1693830870d882a2270d31820482fe6ca562d338a02abee2dc491ca0d06a6de5209c3ff7aa6ad6b195df1ec2a751491bf83f8077e0a976e3e2fa640645eac086
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 78 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: 428 hits
CiteExportLink to record
Permanent link

Direct link
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