Research into recommender systems has focused on the importance of considering a variety of users' inputs for an efficient capture of their main interests. However, most collaborative filtering efforts are related to latent factors and implicit feeback, which do not consider the metadata associated with both items and users. This article proposes a hybrid recommender model which exploits implicit feedback from users by considering not only the latent space of factors that describes the user and item, but also the available metadata associated with content and individuals. Such descriptions are an important source for the construction of a user's profile that contains relevant and meaningful information about his/her preferences. The proposed model is generic enough to be used with many descriptions and types and characterizes users and items with distinguished features that are part of the whole recommendation process. The model was evaluated with the well-known MovieLens dataset and its composing modules were compared against other approaches reported in the literature. The results show its effectiveness in terms of prediction accuracy.