Recommender systems address the information overload problem in the Internet by estimating users’ preferences and recommending items they might like and interact with. For online entities and companies, these tools have become key components of their websites or applications in order to boost activities, enhance customer experience and facilitate users’ decision-making through personalization. The growing availability of online information and the advancements in the field of Deep Neural Networks have determined the transition from traditional methods such as purely content-based or collaborative filtering to hybrid models: capable of improving the recommendation quality, capturing more complex user-item relationships and better in tackling user-item cold start problem.