Antônio Eduardo Rodrigues De Souza, Sandra Maria Dotto Stump, Yara Maria Botti Mendes De Oliveira


Economic globalization has made products and services markets more competitive, demanding a better qualification of skilled manpower. Consequently, companies have needed well qualified professionals to meet specific demands. Specialization courses have been options sought by professionals in order to update the knowledge, in several areas, targeted to specific audience. However, a poorly chosen option can frustrate expectations and incur abandonment or change of chosen course. The purpose of this study is to develop a model of recommendation system based on professional characteristics of candidates, using Artificial Intelligence techniques based on decision trees, which identifies the most appropriate supplemental education to the candidate's profile. The proposed model predicts that the choice of the candidate become more accurate and agile, helping to minimize the number of withdrawal or dropouts from courses options selected as complementary education. Index Terms - Recommender systems, data mining, data filtering techniques, academic counseling.

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This work is licensed under a Creative Commons Attribution 3.0 License.


ISSN 2319-0507



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