User recommendation is a big challenge for collaborative filtering, due to the large number of users a company can have today. In addition, the data to recommend products, entertainment programs, among others, have to be very dispersed, there are not many users sent a vote towards a product, generating a high spread to make a recommendation. To solve this problem, we propose a hybrid system, which uses collaborative filtering and content-based filtering techniques where we use the user's personal information (in this case the occupation or work) to segment them into groups and then use the rating of the items to be able to make predictions. We then make recommendations using aggregation techniques such as average, maximum and minimum, which will be evaluated with metrics such as RMSE and MAE in order to obtain the best recommendation approach. To carry out this hybrid recommendation system, we have used a public database called Movielens. The research aims to leave a baseline for future research so that the proposed system can be improved with different methods.