Time-Sensitive Behavior Prediction in a Health Social Network
Time-Sensitive Behavior Prediction in a Health Social Network
复制标题
健康社交网络中的时间敏感行为预测
DOI：
10.1109/icmla.2017.000-4
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Piniewski, Brigitte
中科院分区：
文献类型：
--
作者：
Amimeur, Amnay;Phan, NhatHai;Dou, Dejing;Kil, David;Piniewski, Brigitte
人类行为预测对于理解和解决在线社区中的大规模健康和社会问题至关重要。具体来说，预测用户在未来何时会参与某个行为，而不是预测用户是否会在特定时间采取行动，是行为预测中研究较少的子问题。更缺乏的是探索社会背景如何影响个人行为和网络结构信息在行为和时间预测的开发。为了解决这些问题，我们提出了一种新的半监督深度学习模型，用于预测个人行为的返回时间。精心设计的目标函数确保模型学习良好的社会背景嵌入和历史行为嵌入，以捕捉社会影响对个人行为的影响。我们的模型在一个独特的健康社交网络数据集上进行了验证，预测用户何时会参与体育活动。我们的模型优于相关的时间预测基线。
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.