A novel hybrid recommender system approach for student academic advising named COHRS, supported by case-based reasoning and ontology

Abstract

The recent development of theWorldWideWeb, 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 recommender 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 Casebased 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?.