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
Comparison and Improvement Of Collaborative Filtering Algorithms
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Jämförelse och förbättring av kollaborativa filtreringsalgoritmer (Swedish)
Abstract [en]

Recommender Systems is a topic several computer scientists have researched. With today’s e-commerce and Internet access, companies try to maximize their profit by utilizing var- ious recommender algorithms. One methodology used in such systems is Collaborative Filtering.

The objective of this paper is to compare four algorithms, all based on Collaborative Filtering, which are k-Nearest-Neighbour, Slope One, Singular Value Decomposition and Average Least Square algorithms, in order to find out which algorithm produce the best pre- diction rates. In addition, the paper will also use two mathematical models, the Arithmetic Median and Weighted Arithmetic Mean, to determine if they can improve the prediction rates.

Singular Value Decomposition performed the best out of the four algorithms and Aver- age Least Square performed the worst. However, the Arithmetic Median performed slightly better than Singular Value Decomposition and the Weighted Arithmetic Mean performed the worst. 

Abstract [sv]

Rekommendationssystem är ett ämne som många datatekniker har forskat inom. Med dagens e-handel och Internetåtkomst, så försöker företag att maximera sina vinster genom att utnyttja diverse rekommendationsalgoritmer. En metodik som används i sådana system är Collaborative Filtering.

Syftet med denna uppsats är att jämföra fyra algoritmer, alla baserade på Collaborati- ve Filtering, vilket är k-Nearest-Neighbour, Slope One, Single Value Decomposition och Average Least Square, i syfte att ta reda på vilken algoritm som producerar den bästa be- tygsättningen. Uppsatsen kommer även använda sig av två olika matematiska modeller, Aritmetisk Median och Viktad Aritmetisk Median, för att ta reda på om dom kan förbättra betygsättningen.

Single Value Decomposition presterade bäst medan Average Least Square presterade sämst av de fyra algoritmerna. Däremot presterade Aritmetiska Median en aning bättre än Single Value Decomposition och Viktad Aritmetisk Median presterade sämst. 

Place, publisher, year, edition, pages
2017.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-209468OAI: oai:DiVA.org:kth-209468DiVA, id: diva2:1112342
Supervisors
Examiners
Available from: 2017-06-21 Created: 2017-06-20 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

fulltext(1297 kB)204 downloads
File information
File name FULLTEXT01.pdfFile size 1297 kBChecksum SHA-512
a167e8e0dca52d3e3a680912613b5856ed97b03ddd0e56965d1e7e05278575aaff138bf3e90a5a18d88541d0266ab9a33d6707f8ae680faba3ed1f0fd46a1597
Type fulltextMimetype application/pdf

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

Search outside of DiVA

GoogleGoogle Scholar
Total: 204 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: 248 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