Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments

Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments

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) and Otavio Costa Acosta (Universidade Federal do Rio Grande do Sul, Brazil)
DOI: 10.4018/978-1-4666-4542-4.ch007
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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 mobile devices, for example, Amazon.com and Submarino.com. In this chapter, the authors propose the use of such systems in the area of education, specifically 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 filtering 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.
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Recommender Systems And Learning Objects

There are several applications of content retrieval which try to assist users in identifying items of interest. However, it is common that these applications bring much irrelevant content as result (Adomavicius & Tuzhilin, 2005). Trying to minimize this problem, recommender systems have emerged, focusing on the search for relevant information in accordance with User's own characteristics, as well as certain requirements relating to the items it want to find.

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