  Diet management is key to managing chronic diseases 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, namely foods users like to and
are able to eat frequently. 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. 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)
