@inproceedings{b9444130d7584e00aabefea26e8c8e62,
title = "Time-sensitive behavior prediction in a health social network",
abstract = "Human behavior prediction is critical in understanding and addressing large scale health and social issues in online communities. Specifically, predicting when in the future a user will engage in a behavior as opposed to whether a user will behave at a particular time is a less studied subproblem of behavior prediction. Further lacking is exploration of how social context affects personal behavior and the exploitation of network structure information in behavior and time prediction. To address these problems we propose a novel semi-supervised deep learning model for prediction of return time to personal behavior. A carefully designed objective function ensures the model learns good social context embeddings and historical behavior embeddings in order to capture the effects of social influence on personal behavior. Our model is validated on a unique health social network dataset by predicting when users will engage in physical activities. We show our model outperforms relevant time prediction baselines.",
keywords = "Behavior Prediction, Deep Learning, Graph Embedding, Social Network Analysis",
author = "Amnay Amimeur and Phan, {Nhat Hai} and Dejing Dou and David Kil and Brigitte Piniewski",
year = "2017",
month = jan,
day = "1",
doi = "10.1109/ICMLA.2017.000-4",
language = "English (US)",
series = "Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1083--1088",
editor = "Xuewen Chen and Bo Luo and Feng Luo and Vasile Palade and Wani, {M. Arif}",
booktitle = "Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017",
address = "United States",
note = "16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 ; Conference date: 18-12-2017 Through 21-12-2017",
}