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A Hybrid Recommender: Study and implementation of course selection  recommender engine
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis project is a theoretical and practical study on recommender systems (RSs). It aims to help the planning of course selection for students from the Master Programme in Computer Science in Uppsala University. To achieve the goal, the project implements a recommender service, which generates course selection recommendations based on these three factors:     (i) student users’ preferences     (ii)  course requirements from the university     (iii) best practices from senior students The implementation of the recommender service takes these three approaches:      applying frequent-pattern mining techniques on senior students’ course selection data ,  performing semantic queries on a simple knowledge organization system (SKOS) taxonomy file that classifies computing disciplines, applying constraint programming (CP) techniques for problem modelling and resolving when generating final course selection recommendations     The recommender service is implemented as a representational state transfer (REST) compliant web service, i.e., a RESTful web service. The result shows that aforementioned factors have positive impact on the output of the service. Preliminary user feedback gives encouraging rating on the quality of the recommendations.     This report will talk about recommender systems, the semantic web, constraint programming and the implementation details of the recommender service. It focuses on in-depth discussion of recommender systems and the recommender service’s implementation. 

Place, publisher, year, edition, pages
2017. , p. 104
Series
IT ; 17011
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-321193OAI: oai:DiVA.org:uu-321193DiVA, id: diva2:1092321
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2017-05-02 Created: 2017-05-02 Last updated: 2017-05-05Bibliographically approved

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CiteExportLink to record
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  • apa
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