Projeto e desenvolvimento de um algoritmo de recomendação aplicado ao sistema science

Abstract

Nowadays, a big part from the softwares that provides some service, relies on a recommender system. A recommender system can be defined as an algorithm that generates a recommendation, based, weather on the ratings from the users, or based on the content from the service. The algorithms are different on its filtering model. There are three kinds of data filtering, collaborative filtering, content-based filtering, and hybrid filtering, we are going to use the content base filtering on this recommender system development for improving the SCIENCE. The problem taken in this article, was the kNN (k-Nearest Neighbours), unsupervised machine learning algorithm. The metric used, was the cosine similarity, for that reason, the closest similarity possible, based on the angle of similarity between two vectors. This given project, presents the development and integration of recommender system using content-based filtering, and the development of future algorithms that uses collaborative filtering.