A Hybrid Recommender: Study and implementation of course selection  recommender engine

Detta är en Master-uppsats från Uppsala universitet/Institutionen för informationsteknologi

Författare: Huang Yong; [2017]

Nyckelord: ;

Sammanfattning: 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. 

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