Regularização social em sistemas de recomendação com filtragem colaborativa

Abstract

Models based on matrix factorization are among the most successful implementations of Recommender Systems. In this project, we study the possibilities of incorporating the information from social networks to improve the quality of predictions of the model both in traditional Collaborative Filtering and in Neural Collaborative Filtering. Based on four examples, we registered that incorporating information from the social network in fact leads to better estimates of the evaluations of itens by users.