Modeling Non-deterministic Human Behaviors in Discrete Food Choices

Andrew Starnes, Anton Dereventsov, E. S. Blazek, Folasade Phillips
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Abstract

We establish a non-deterministic model that predicts a user's food preferences from their demographic information. Our simulator is based on NHANES dataset and domain expert knowledge in the form of established behavioral studies. Our model can be used to generate an arbitrary amount of synthetic datapoints that are similar in distribution to the original dataset and align with behavioral science expectations. Such a simulator can be used in a variety of machine learning tasks and especially in applications requiring human behavior prediction.
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离散食物选择中的非确定性人类行为建模
我们建立了一个非确定性模型,根据用户的人口统计信息预测他们的食物偏好。我们的模拟器基于NHANES数据集和已建立的行为研究形式的领域专家知识。我们的模型可用于生成任意数量的合成数据点,这些数据点在分布上与原始数据集相似,并符合行为科学的期望。这样的模拟器可以用于各种机器学习任务,特别是在需要人类行为预测的应用中。
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