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.