  Recommendation Systems (SR) suggest items exploring user preferences, helping
them with the information overload problem. Two approaches to SR have received
more prominence, Collaborative Filtering, and Content-Based Filtering.
Moreover, even though studies are indicating their advantages and
disadvantages, few results empirically prove their characteristics,
similarities, and differences. In this work, an experimental methodology is
proposed to perform comparisons between recommendation algorithms for different
approaches going beyond the "precision of the predictions". For the
experiments, three algorithms of recommendation were tested: a baseline for
Collaborative Filtration and two algorithms for Content-based Filtering that
were developed for this evaluation. The experiments demonstrate the behavior of
these systems in different data sets, its main characteristics and especially
the complementary aspect of the two main approaches.
