Multi-Modality Behavioral Influence Analysis for Personalized Recommendations in Health Social Media Environment

Recently, health social media have engaged more and more people to share their personal feelings, opinions, and experience in the context of health informatics, which has drawn increasing attention from both academia and industry. In this paper, we focus on the behavioral influence analysis based on heterogeneous health data generated in social media environments. An integrated deep neural network (DNN)-based learning model is designed to analyze and describe the latent behavioral influence hidden across multiple modalities, in which a convolutional neural network (CNN)-based framework is used to extract the time-series features within a certain social context. The learned features based on cross-modality influence analysis are then trained in a SoftMax classifier, which can result in a restructured representation of high-level features for online physician rating and classification in a data-driven way. Finally, two algorithms within two representative application scenarios are developed to provide patients with personalized recommendations in health social media environments. Experiments using the real world data demonstrate the effectiveness of our proposed model and method.