The main goal of this project was to implement a recommender system for electronic games available on Steam, based in buying data and time dedicated by users for playing games. Different neighborhood based collaborative filtering techniques were used, which provided better results than the latent factors models-based approach used. Different metrics for ranking evaluation by utility were used. Furthermore, five approaches to generate explicit ratings on a scale of 1 to 5 from de data available were tested and evaluated.