Retweeting Behavior Prediction Using Probabilistic Matrix Factorization

Retweeting is an important mechanism for information diffusion, popular event prediction, and so on. Due to the increasing requirements, in recent years, the task has attracted extensive attentions. In this paper, we propose a novel framework using probabilistic matrix factorization technique to predict retweeting behavior. Our study consists of three components. First, we convert retweeting behavior problem to a matrix factorization problem. Second, following the intuition that a user's social network will affect his retweeting behavior, we extensively study how to model social information to improve the prediction accuracy. Finally, message semantic embedding information is employed in designing a semantic regularization term to constrain the matrix factorization objective function. We also propose a set of metrics to construct the embeddings among messages based on messages' structural and textual features. The empirical results and analysis demonstrate that our methods perform better than the state-of-the-art approaches.