As a means to understanding the healthiness of the food environment, obtaining big data (big food and other types) to model the built environment becomes critical. In this paper, we train and test seven different ML methods on bigdata from census data to predict the healthiness of the food environment. We introduce a synthetic ecosystem platform that can be used to bridge big data of different types combined with ML method for supporting food environment surveillance and intervention simulations. We illustrate with an example of neighborhood-level healthfulness assessment and conclude by a presentation of our next steps on employing machine learning to classify diet quality and recommend healthier food options to consumers.

The supply and demand of fresh food products must be tightly integrated to mitigate food waste, economic losses, and expansion of the environmental footprint. In this study, we use a novel loyalty program dataset from a grocery retailer in Quebec, Canada to predict demand for yogurt products for 17 months from 2015 to 2016. Focusing our attention on 13 newly launched yogurt products from a local manufacturer, we build and test 18 different machine learning models capable of predicting demand for individual products at the aggregate market level, as well as for each store. Store-level data were matched to neighborhood demographic data from the 2016 Canadian census to enrich features. Overall, 330 features were engineered to provide information on the product, marketing and promotions, store, and neighborhood over time. Analyses were conducted using Python 3 in Google Collaboratory and open-source libraries. Results from the best market-level model (random forest) achieve an r-squared of 84.0% on test data, while the store-level model (light gradient boosting machine) only achieves 57%. The results show that ML tools can be useful in modeling demand for new products at aggregate levels but achieving accurate predictions at more granular levels remains a hurdle to overcome. Insights for the preparation and analysis of loyalty data are discussed.

Typical recommender systems try to mimic the past behaviors of users to make future recommendations. For example, in food recommendations, they tend to recommend the foods the user prefers. While the recommended foods may be easily accepted by the user, it cannot improve the user’s dietary habits for a specific goal such as weight control. In this paper, we build a food recommendation system that can be used on the web or in a mobile app to help users meet their goals on body weight, while also taking into account their health information (BMI) and the nutrition information of foods (calories). Instead of applying dietary guidelines as constraints, we build recommendation models from the successful behaviors of comparable users: the weight loss model is trained using the historical food consumption data of similar users who successfully lost weight. By combining such a goal-oriented recommendation model with a general model, the recommendations can be smoothly tuned toward the goal without disruptive food changes. We tested the approach on real data collected from a popular weight management app. It is shown that our recommendation approach can better predict the foods for test periods where the user truly meets the goal, than the typical existing approaches.