In the field of machine learning, the practicality and effectiveness of the Adaboost algorithm has already been demonstrated. However, since this algorithm is originally designed for classification problems, it cannot be applied directly to rating prediction problems in recommender system field. Thus the research in this area is limited. In this paper, we improve the Adaboost algorithm. By introducing the threshold value, we transform rating prediction into classification. By updating weights in the training process, we propose a framework for the rating prediction, which can integrate the multiple training models. The final rating is obtained through the integrated model. We select the Matrix Factorization model as an instance, and the experimental results show that the framework can effectively improve the prediction accuracy.