Learning Personal Food Preferences via Food Logs Embedding
This work addresses the need for more accurate food recommender systems to assist in diet management for individuals with chronic diseases like diabetes, though it is incremental in nature.
The paper tackles the problem of learning personal food preferences from noisy food logs to improve automated food recommendation systems, achieving 82% accuracy in identifying a user's ten most frequently eaten foods.
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)