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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 thesisAlternative title
En Hybridstrategi till Rekommendationssystem : Inhehållsförstärkt kollaborativ filtrering (Swedish)
Abstract [en]

Recommender systems help shape the way the internet is used by leading users directly to the content which will interest them most. Traditionally, collaborative recommender systems based purely on user ratings have been proven to be effective. This report focuses specifically on film recommender systems. It investigates how the film content parameters Actor, Director and Genre can be used to further enhance the accuracy of predictions made by a purely collaborative approach, specifically with regards to the set of films chosen when performing the prediction calculations. The initial results showed that relying solely on content in this selection led to poorer predictions due to a lack of ratings. However, the investigation finds that using a hybrid approach between the two selection techniques with a bias for content solved this problem as well as increasing the overall prediction accuracy by over 11%. 

Place, publisher, year, edition, pages
National Category
Computer Science
URN: urn:nbn:se:kth:diva-186337OAI: diva2:926975
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2016-05-18 Created: 2016-05-10 Last updated: 2016-05-18Bibliographically approved

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