Item recommendation from implicit feedback datasets consists of passively tracking different sorts of user behavior, such as purchase history, watching habits and browsing activities in order to improve customer experience through providing personalized recommendations that fits into users taste. In this work we evaluate the performance of different matrix factorization models tailored for the recommendation task for the implicit feedback dataset extracted from Globo.com s video site s access logs. We propose treating the data as indication of a positive preference from a user regarding the video watched. Besides that we evaluated the impact of effects associated with either users or items, known as biases or intercepts, independent of any interactions and its time changing behavior throughout the life span of the data in the result of recommendations. We also suggest a scalable and incremental procedure, which scales linearly with the input data size. In trying to predict the intention of the users for consuming new videos our best factorization models achieves a RMSE of 0,0524 using user s and video s bias as well as its temporal dynamics.