Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Category Preferred Canopy-K-means based Collaborative Filtering algorithm
Department of Computer Science and Technology, University of Science and Technology, Beijing.
Department of Computer Science and Technology, University of Science and Technology, Beijing.
Department of Computer Science and Technology, University of Science and Technology, Beijing.
Department of Computer Science and Technology, University of Science and Technology, Beijing.
Visa övriga samt affilieringar
2019 (Engelska)Ingår i: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 93, s. 1046-1054Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

It is the era of information explosion and overload. The recommender systems can help people quickly get the expected information when facing the enormous data flood. Therefore, researchers in both industry and academia are also paying more attention to this area. The Collaborative Filtering Algorithm (CF) is one of the most widely used algorithms in recommender systems. However, it has difficulty in dealing with the problems of sparsity and scalability of data. This paper presents Category Preferred Canopy-K-means based Collaborative Filtering Algorithm (CPCKCF) to solve the challenges of sparsity and scalability of data. In particular, CPCKCF proposes the definition of the User-Item Category Preferred Ratio (UICPR), and use it to compute the UICPR matrix. The results can be applied to cluster the user data and find the nearest users to obtain prediction ratings. Our experimentation results performed using the MovieLens dataset demonstrates that compared with traditional user-based Collaborative Filtering algorithm, the proposed CPCKCF algorithm proposed in this paper improved computational efficiency and recommendation accuracy by 2.81%.

Ort, förlag, år, upplaga, sidor
Elsevier, 2019. Vol. 93, s. 1046-1054
Nationell ämneskategori
Medieteknik
Forskningsämne
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-68939DOI: 10.1016/j.future.2018.04.025ISI: 000459365800085Scopus ID: 2-s2.0-85049085827OAI: oai:DiVA.org:ltu-68939DiVA, id: diva2:1210435
Anmärkning

Validerad;2019;Nivå 2;2019-03-27 (inah)

Tillgänglig från: 2018-05-28 Skapad: 2018-05-28 Senast uppdaterad: 2019-03-27Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Personposter BETA

Mitra, Karan

Sök vidare i DiVA

Av författaren/redaktören
Mitra, Karan
Av organisationen
Datavetenskap
I samma tidskrift
Future generations computer systems
Medieteknik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 90 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf