Abstract
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.
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Notes
Scale-Free/Power Law Model (SF) is a network model whose node degrees follow the Power law distribution, or at least asymptotically.
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Acknowledgments
This work is supported by the NIH Grant R01GM103309 to the SMASH project. We thank Xiao Xiao, Rebeca Sacks, and Ellen Klowden for their contributions. Dr. Phan currently is an Assistant Professor at New Jersey Institute of Technology. The work was done when he was a Research Associate at the University of Oregon.
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Phan, N., Dou, D., Piniewski, B. et al. A deep learning approach for human behavior prediction with explanations in health social networks: social restricted Boltzmann machine (SRBM+). Soc. Netw. Anal. Min. 6, 79 (2016). https://doi.org/10.1007/s13278-016-0379-0
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DOI: https://doi.org/10.1007/s13278-016-0379-0