Time-sensitive behavior prediction in a health social network

Document Type

Conference Proceeding

Publication Date

1-1-2017

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.

Identifier

85048464059 (Scopus)

ISBN

[9781538614174]

Publication Title

Proceedings 16th IEEE International Conference on Machine Learning and Applications Icmla 2017

External Full Text Location

https://doi.org/10.1109/ICMLA.2017.000-4

First Page

1083

Last Page

1088

Volume

2017-December

Grant

1650587

Fund Ref

National Science Foundation

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