Social Media Prediction Based on Residual Learning and Random Forest

In this paper, we propose a unified framework for the residual learning and random forest regression for social media prediction task. Given a post including photo and its social information, the primary goal is to predict the view count of the post. In this regression problem, we first predict the view count based on random forest regressor for the social information. Since regressor tends to learn a relative soothingness model to avoid overfitting, the extreme high/low view counts of the poses are hard to predict. We solve this problem by using residual learning to refine the prediction. Based on this initial prediction, the residual value of the prediction and its ground truth is calculated. Then, the image and its social information will feed to 13-layers ResNet to predict the residual value to compensate the initial prediction for extreme high/low view counts. Experiments show that the performance of the proposed method significantly outperforms other methods.