Modeling Non-deterministic Human Behaviors in Discrete Food Choices
This work addresses the need for synthetic data in machine learning tasks involving human behavior prediction, particularly in food choices, but it is incremental as it builds on existing datasets and expert knowledge.
The authors tackled the problem of predicting human food preferences from demographic data by establishing a non-deterministic model based on the NHANES dataset and behavioral studies, resulting in a simulator that generates synthetic data similar to the original distribution and aligned with behavioral science.
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.