Ontology-based personalised course recommendation framework

Mohammed E. Ibrahim, Yanyan Yang, David Ndzi, Guangguang Yang, Murtadha Almaliki

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Choosing a higher education course at university is not an easy task for students. A wide range of courses is offered by individual universities whose delivery mode and entry requirements all differ. A personalised recommendation system can be an effective way of suggesting relevant courses to prospective students. This paper introduces a novel approach that personalises course recommendations that will match the individual needs of users. The proposed approach developed a framework of an ontology-based hybrid-filtering system called OPCR. This approach aims to integrate information from multiple sources based on hierarchical ontology similarity with a view to enhancing efficiency and user satisfaction and to provide students with appropriate recommendations. OPCR combines collaborative based filtering with content-based filtering. It also considers familiar related concepts that are evident in the profiles of both the student and the course, determining the similarity between them. Furthermore, OPCR uses an ontology mapping technique, recommending jobs that will be available following completion of each course. This method can enable students to gain a comprehensive knowledge of courses based on their relevance, using dynamic ontology mapping to link course profiles and student profiles with job profiles. Results show that a filtering algorithm that uses hierarchically related concepts produces better outcomes compared to a filtering method that considers only keyword similarity. In addition, the quality of the recommendations improved when the ontology similarity between the items’ profiles and the users’ profiles were utilised. This approach, using a dynamic ontology mapping, is flexible and can be adapted to different domains. The proposed framework can be used to filter items for both postgraduate courses and items from other domains.

Original languageEnglish
JournalIEEE Access
Early online date24 Dec 2018
Publication statusEarly online - 24 Dec 2018


  • Course Recommender system
  • Education Domain
  • Information Overload
  • Ontology
  • Recommendation Systems


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