Recommender systems represent tools capable of providing suggestions based on large datasets or collections of objects in the user’s area of interest. Career recommendations have become more and more popular, as they are able to offer guidance and help people build their most suitable future professional path. Without any doubt, electronic profiling can improve the automatization and accuracy of the recommendation process. Our paper presents the process of electronic profiling used in the CareProfSys research project; a smart career profiler based on semantic data fusion. The research methodology includes the analysis of a job survey filled in by university students, containing similar information as that included in a LinkedIn user profile. In a detailed case study, we further perform the analysis of the web and multimedia systems developer job which is of interest to most respondent students. All results and conclusions will be used in improving the construction of the user profile and the recommendation process of the CareProfSys.
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