The most important task of a data scientist is to predict from available data. A lot of algorithms are been proposed to be used as a recommender system given the know preferences of users, but they don’t use the attributes of them (gender, age …). In our work we try to answer an important question: Which information is useful to improve this predictions? Are there any correlation with the gender of people and their preferences?
To answer this question, we take an algorithm based on a bayesian framework that our group implemented and we extend it adding this attributes to make predictions.