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Cold-start recommendations for the user- and item-based recommender systemalgorithm k-Nearest Neighbors
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 thesis
Abstract [en]

Recommender systems apply machine learning methods to solve the task of providing appropriate suggestions to users in both static and dynamic environments. An example of this is a movie service like Netflix that recommends movies to its users. Although many algorithms have been proposed, making predictions for users with few ratings remains a challenge in recommender systems.

In this study the performance of the algorithm k-NN, both user- and item-based, was empirically evaluated. This was done using the MovieLens 1M and 100K datasets in scenarios where the users have between 1 and 9 ratings, simulating cold-start scenarios of various degree. The results were then compared with the accuracy of the algorithm in a simulated normal case, to see how the cold-start affected the two algorithms, and which one of them that handled it best.

In summary, this report shows that user-based k-NN performs better in relation to item-based k-NN for new users having few rated items. Overall the accuracy improved as the number of ratings increased for the new users for both user- and item-based k-NN.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Cold start, Recommender system, K-NN
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-208661OAI: oai:DiVA.org:kth-208661DiVA: diva2:1107752
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2017-06-13 Created: 2017-06-10 Last updated: 2017-06-13Bibliographically approved

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School of Computer Science and Communication (CSC)
Computer Science

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CiteExportLink to record
Permanent link

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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