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This paper describes an approach for incorporating externally specified aggregate ratings information into certain types of collaborative filtering (CF) methods. For a statistical model-based CF approach, we formally showed that this additional aggregated information provides more accurate recommendations of individual items to individual users. Furthermore, theoretical insights gained from the analysis of this model-based method suggested a way to incorporate aggregate information into the heuristic item-based CF method. Both the model-based and the heuristic item-based CF methods were empirically tested on several datasets, and the experiments uniformly confirmed that the aggregate rating information indeed improves CF recommendations. These results also show the power of theory by demonstrating how the insights gained from theoretical developments can shed light on proper selection of good heuristic methods. We also showed the way to introduce scalability and parallelization into the estimation procedure and reported the running time for steps of the estimation procedure for large datasets.