The recent development of the WorldWideWeb, 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 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.