Using Ontology for Personalised Course Recommendation Applications

The primary data source for universities and courses for students is increasingly becoming the web, and with a vast amount of information about thousands of courses on different websites, it is quite a task to find one that matches a student’s needs. That is why we are proposing the “Course Recommendation System”, a system that suggests the course best suited for prospective students. As there has been a huge increase in course content on the Internet, finding the course you really need has become time-consuming, so we are proposing to use an ontology-based approach to semantic content recommendation. The aim is to enhance the efficiency and effectiveness of providing students with suitable recommendations. The recommender takes into consideration knowledge about the user (the student’s profile) and course content, as well as knowledge about the domain that is being learned. Ontology is used to both models and represent such forms of knowledge. There are four steps to this: extracting information from multiple sources, applying ontologies by using Protege tools, semantic relevance calculation and refining the recommendation. A personalised, complete and augmented course is then suggested for the student, based on these steps.

[1]  Jorge S. Cardoso.  The Semantic Web Vision: Where Are We? , 2007, IEEE Intelligent Systems.

[2]  Khaleel Ur Rahman Khan,et al.  Ontological student profile , 2012, CCSEIT '12.

[3]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[4]  Kun Hua Tsai,et al.  A Learning Objects Recommendation Model based on the Preference and Ontological Approaches , 2006, Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06).

[5]  Kevin Curran,et al.  Context-aware intelligent recommendation system for tourism , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[6]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[7]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[8]  Rung Ching Chen,et al.  Course-recommendation system based on ontology , 2013, 2013 International Conference on Machine Learning and Cybernetics.

[9]  Hongji Yang,et al.  Extracting ontologies from legacy systems for understanding and re-engineering , 1999, Proceedings. Twenty-Third Annual International Computer Software and Applications Conference (Cat. No.99CB37032).

[10]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[11]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[12]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[13]  Zhiwen Yu,et al.  Ontology-Based Semantic Recommendation for Context-Aware E-Learning , 2007, UIC.

[14]  Nana Yaw Asabere.  Towards a Viewpoint of Context-Aware Recommender Systems (CARS) and Services , 2013 .

[15]  Jie Lu,et al.  A Course Recommender System Using Multiple Criteria Decision Making Method , 2007 .

[16]  Yuzuru Tanaka,et al.  Topic-oriented query expansion for web search , 2006, WWW '06.

[17]  José Eduardo Ochoa Luna,et al.  Online Courses Recommendation based on LDA , 2014, SIMBig.

[18]  Peter Brusilovsky,et al.  Social Navigation Support in a Course Recommendation System , 2006, AH.

[19]  Dan Gusfield,et al.  Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology , 1997 .

[20]  Yi-Cheng Zhang,et al.  Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs , 2009, ArXiv.

[21]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[22]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.