With growing number of users in Colla, maintaining a single model in cloud may not achieve a satisfied prediction accuracy for all users as different groups of users may have very different behavior patterns. To solve this problem, instead of using a one-fits-all cloud model, Colla splits up the cloud model over time to serve different groups of users. Based on N output feature vectors, we use the cross entropy between two feature vectors as the distance of two models, and cluster models into K groups using the Affinity Propagation.