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Using Demographic Information to Reduce the New User Problem in Recommender Systems
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
Användning av Demografisk Information för att minska Ny Användar problemet i Rekommendationssystem (Swedish)
Abstract [en]

Recommender systems rely heavily on user data to make accurate rec- ommendations. This presents a problem for new users for whom no such data is available. This study investigated if this problem could be reduced by basing recommendations solely on user’s demographic in- formation. Experiments were conducted using a framework that em- ploys K-means clustering. To evaluate the framework, the MovieLens 100K dataset was applied to a set of experiments. While the results did not exhibit any correlation between ratings and demographic features in the MovieLens 100K dataset, it does not exclude that the framework is not effective on other datasets with more demographic features. 

Abstract [sv]

Rekommendationssystem förlitar sig starkt på användardata för att göra korrekta rekommendationer. Detta medför ett problem för nya användare för vilka det inte finns någon sådan data tillgängligt. Den- na studie undersökte om detta problem kunde minskas genom att ba- sera rekommendationer enbart på demografisk användarinformation. Experimenten utfördes med ett nytt ramverk som använder K-means klustering. För att evaluera ramverket tillämpades MovieLens 100K datasetet på ett antal experiment. Även fast resultatet inte uppvisade någon korrelation mellan filmbetyg och användaregenskaper i Movie- Lens 100K datasetet, så motsäger det inte att ramverket inte är effektivt på andra dataset med mer demografisk data. 

Place, publisher, year, edition, pages
2017.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-209332OAI: oai:DiVA.org:kth-209332DiVA, id: diva2:1111527
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2017-06-19 Created: 2017-06-19 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
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  • apa
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