It is today accepted that matrix factorization models allow a high quality of rating prediction in recommender systems. However, a major drawback of matrix factorization is its static nature that results in a progressive declining of the accuracy of the predictions after each factorization. This is due to the fact that the new obtained ratings are not taken into account until a new factorization is computed, which can not be done very often because of the high cost of matrix factorization. In this paper, aiming at improving the accuracy of recommender systems, we propose a cluster-based matrix factorization technique that enables online integration of new ratings. Thus, we significantly enhance the obtained predictions between two matrix factorizations. The experiments we did on a large dataset demonstrated the efficiency of our approach. Furthermore, we devise an experimental protocol that allows for identifying the optimal number of ratings to take into account for factorization. This contributes as a practical solution addressing the tradeoff between taking into account more ratings into factorization for better quality, versus missing fewer ratings during factorization.