A deep learning approach for human behavior prediction with explanations in health social networks: social restricted Boltzmann machine (SRBM⁺)

Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and the likelihood that the systems will be actually adopted, thus driving engagement and loyalty. However, most prediction models do not provide explanations for the behaviors they predict. In this paper, we study the research problem, human behavior prediction with explanations, for healthcare intervention systems in health social networks. In this work, we propose a deep learning model, named social restricted Boltzmann machine (SRBM), for human behavior modeling over undirected and nodes-attributed graphs. In the proposed SRBM⁺ model, we naturally incorporate self-motivation, implicit and explicit social influences, and environmental events together. Our model not only predicts human behaviors accurately, but also, for each predicted behavior, it generates explanations. Experimental results on real-world and synthetic health social networks confirm the accuracy of SRBM⁺ in human behavior prediction and its quality in human behavior explanation.