Title: Towards effective course-based recommendations for public tenders

Authors: Frederico Durao; Marcel Pinheiro Caraciolo; Bruno Melo; Silvio Romero de Lemos Meira

Addresses: Federal University of Bahia, Av. Adhemar de Barros, s/n – Campus de Ondina, Salvador, Bahia, CEP 40.170-110, Brasil ' Atepassar.com, Rua Jacobina, 83 Graças, Recife, Pernambuco, CEP: 52011-180, Brasil ' PreciseSkills Inc., P.O. Box 14021 104 T W Alexander Dr, Bldg. 1 Durham, NC 27709, USA ' Federal University of Pernambuco, Rua do Apolo, 161, Recife Antigo, Recife-PE. CEP – 50030-220, Brasil

Abstract: In this paper, we propose a recommendation model to assist users find relevant courses for public tenders. The recommendations are computed based on the user study activity at Atepassar.com, a web-based learning environment for public tender candidates. Unlike traditional academic-oriented recommender systems, our approach takes into account crucial information for public tender candidates such as salary offered by public tenders and location where the exams take place. Technically, our recommendations rely on content-based techniques and a location reasoning method in order to provide users with most feasible courses. Results from a real-world dataset indicate reasonable improvement in recommendation quality over compared baseline models - we observed about 11 precision improvement and 12.7% of recall gain over the best model compared - demonstrating the potential of our approach in recommending personalised courses.

Keywords: recommendation models; e-learning; course-based recommendation systems; public tenders; education; electronic learning; online learning; course selection; recommender systems; video lectures; public exams; Brazil.

DOI: 10.1504/IJKWI.2013.056374

International Journal of Knowledge and Web Intelligence, 2013 Vol.4 No.2/3, pp.260 - 278

Received: 20 Sep 2012
Accepted: 27 May 2013

Published online: 26 Jul 2014 *

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