Learning Personal Food Preferences via Food Logs Embedding

Diet management is key to managing chronic dis-eases such as diabetes. Automated food recommender systems may be able to assist by providing meal recommendations that conform to a user’s nutrition goals and food preferences. Current recommendation systems suffer from a lack of accuracy that is in part due to a lack of knowledge of food preferences. In this work, we propose a method for learning food preferences from food logs, a comprehensive but noisy source of information about users’ dietary habits. We also introduce accompanying metrics to evaluate personal learning food preferences. The method generates and compares word embeddings to identify the parent food category of each food entry and then calculates the most popular. Our proposed approach identifies 82% of a user’s ten most frequently eaten foods. Our method is publicly available on (https://github.com/aametwally/LearningFoodPreferences).