Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named socialized Gaussian process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals’ behaviors influenced by their friends’ previous behaviors, SGP models the dynamic social correlation as the result of social influence. The SGP model naturally incorporates personal behavior factor and social correlation factor (i.e., the homophily principle: Friends tend to perform similar behaviors) into a unified model. And it models the social influence factor (i.e., an individual’s behavior can be affected by his/her friends) implicitly in dynamic social correlation schemes. The detailed experimental evaluation has shown the SGP model achieves better prediction accuracy compared with most of baseline methods. However, a Socialized Random Forest model may perform better at the beginning compared with the SGP model. One of the main reasons is the dynamic social correlation function is purely based on the users’ sequential behaviors without considering other physical activity-related features. To address this issue, we further propose a novel “multi-feature SGP model” (mfSGP) which improves the SGP model by using multiple physical activity-related features in the dynamic social correlation learning. Extensive experimental results illustrate that the mfSGP model clearly outperforms all other models in terms of prediction accuracy and running time.

中文翻译：

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用于健康社交网络中人类行为预测的动态社会化高斯过程模型。


建模和预测人类行为（例如体力活动的水平和强度）是防止肥胖连锁反应和帮助在社交网络中传播健康行为的关键。在我们的会议论文中，我们开发了一种社会影响模型，称为社会化高斯过程（SGP），用于社会化人类行为建模。 SGP 没有将社会影响力明确地建模为受朋友先前行为影响的个人行为，而是将动态社会相关性建模为社会影响力的结果。 SGP模型自然地将个人行为因素和社会关联因素（即同质原则：朋友倾向于做出相似的行为）纳入统一的模型中。它隐式地对动态社会相关方案中的社会影响因素（即，个人的行为可能受到他/她的朋友的影响）进行建模。详细的实验评估表明，与大多数基线方法相比，SGP模型具有更好的预测精度。然而，与 SGP 模型相比，社会化随机森林模型在开始时可能表现更好。主要原因之一是动态社交关联函数纯粹基于用户的顺序行为，而没有考虑其他与身体活动相关的特征。为了解决这个问题，我们进一步提出了一种新颖的“多特征SGP模型”（mfSGP），它通过在动态社会相关性学习中使用多个与身体活动相关的特征来改进SGP模型。大量的实验结果表明，mfSGP 模型在预测精度和运行时间方面明显优于所有其他模型。