2159 Copyright © 2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 110 DOI: 10.4018/978-1-4666-8789-9.ch110 Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments ABSTRACT Mobile learning is about increasing learners’ capability to carry their own learning environment along with them. Recommender Systems are widely used nowadays, especially in e-commerce sites and mo- bile devices, for example, Amazon.com and Submarino.com. In this chapter, the authors propose the use of such systems in the area of education, specifcally for the recommendation of learning objects in mobile devices. The advantage of using Recommender Systems in mobile devices is that it is an easy way to deliver recommendations to students. Based on this scenario, this chapter presents a model of a recommender system based on information fltering for mobile environments. The proposed model was implemented in a prototype aimed to recommend learning objects in mobile devices. The evaluation of the received recommendations was conducted using a Likert scale of 5 points. At the end of this chapter, some future works are described. Sílvio César Cazella Universidade Federal des Ciências da Saúde de Porto Alegre, Brazil & Universidade do Vale do Rio dos Sinos, Brazil Jorge Luiz Victória Barbosa Universidade do Vale do Rio dos Sinos, Brazil Eliseo Berni Reategui Universidade Federal do Rio Grande do Sul, Brazil Patricia Alejandra Behar Universidade Federal do Rio Grande do Sul, Brazil Otavio Costa Acosta Universidade Federal do Rio Grande do Sul, Brazil