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As social networking is moving into the web, the study and exploitation of social correlation has emerged as a hot research topic. Most of these work consider binary social relations, called "friendships". However, online users tend to establish many friendships of varying degree of strength, e.g., relatives, friends, co-workers, and acquaintances. We argue that, due to their different degree of strength, different friend relationships will have greatly varying degrees of correlation and should be distinguished. Besides, social correlation is not the only factor driving user behavior. In this paper, we address the problem of learning the strength of the social correlation, user, item, and sparsity factors in online social networks. We propose a probabilistic model, Factor Weight Model, for learning these strengths which maximize the joint probability of the observed user behavior, i.e., actions on items. Different from existing methods, our model considers not only social correlation, but it also considers the other factors affecting user behavior. We have conducted experiments on four real life data sets from Epinions, Flixster, Flickr, and Digg. Our experiments prove the superiority of our model over a state-of-the-art method in terms of action prediction. We also analyze the contributions of the various factors for the prediction performance.