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Diet management is key to managing chronic diseases such as diabetes.Automated food recommender systems may be able to assist by providing mealrecommendations that conform to a user's nutrition goals and food preferences.Current recommendation systems suffer from a lack of accuracy that is in partdue to a lack of knowledge of food preferences, namely foods users like to andare able to eat frequently. In this work, we propose a method for learning foodpreferences from food logs, a comprehensive but noisy source of informationabout users' dietary habits. We also introduce accompanying metrics. The methodgenerates and compares word embeddings to identify the parent food category ofeach food entry and then calculates the most popular. Our proposed approachidentifies 82% of a user's ten most frequently eaten foods. Our method ispublicly available on (https://github.com/aametwally/LearningFoodPreferences)

Learning Personal Food Preferences via Food Logs Embedding