A Novel Hybrid Recommender System Approach for Student Academic Advising Named COHRS, Supported by Case-based Reasoning and Ontology

Charbel Obeid1, Christine Lahoud2, Hicham El Khoury3 and Pierre-Antoine Champin4

  1. LIRIS, Caude Bernard University Lyon 1
    France
    cobeid@liris.cnrs.fr
  2. French University in Egypt
    Egypt
    Christine.lahoud@ufe.edu.eg
  3. LaRRIS, Lebanese University
    Lebanon
    hkhoury@ul.edu.lb
  4. LIRIS, Caude Bernard University Lyon 1
    France
    Pierre-antoine.champin@univ-lyon1.fr

Abstract

The recent development of the World Wide Web, information, and communications technology have transformed the world and moved us into the data era resulting in an overload of data analysis. Students at high school use, most of the time, the internet as a tool to search for universities/colleges, university’s majors, and career paths that match their interests. However, selecting higher education choices such as a university major is a massive decision for students leading them, to surf the internet for long periods in search of needed information. Therefore, the purpose of this study is to assist high school students through a hybrid recom-mender system (RS) that provides personalized recommendations related to their interests. To reach this purpose we proposed a novel hybrid RS approach named (COHRS) that incorporates the Knowledge base (KB) and Collaborative Filtering (CF) recommender techniques. This hybrid RS approach is supported by the Case-based Reasoning (CBR) system and Ontology. Hundreds of queries were processed by our hybrid RS approach. The experiments show the high accuracy of COHRS based on two criteria namely the accuracy of retrieving the most similar cases and the accuracy of generating personalized recommendations. The evaluation results show the percentage of accuracy of COHRS based on many experiments as follows: 98 percent accuracy for retrieving the most similar cases and 95 percent accuracy for generating personalized recommendations.

Key words

Knowledge base, Collaborative Filtering, Hybrid Recommender System, Case-based Reasoning, Ontology

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS220215011O

Publication information

Volume 19, Issue 2 (June 2022)
Year of Publication: 2022
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Obeid, C., Lahoud, C., Khoury, H. E., Champin, P.: A Novel Hybrid Recommender System Approach for Student Academic Advising Named COHRS, Supported by Case-based Reasoning and Ontology. Computer Science and Information Systems, Vol. 19, No. 2, 979–1005. (2022), https://doi.org/10.2298/CSIS220215011O