ACM Transactions on Information Systems
Nutrient-based meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people's food preferences and making recommendations that simultaneously appeal to their palate and satisfy nutritional expectations are challenging. Existing approaches either only learn high-level preferences or require a prolonged learning period. We propose Yum-me, a personalized nutrient-based meal recommender system designed to meet
... uals' nutritional expectations, dietary restrictions, and ne-grained food preferences. Yum-me enables a simple and accurate food preference pro ling procedure via a visual quiz-based user interface, and projects the learned pro le into the domain of nutritionally appropriate food options to nd ones that will appeal to the user. We present the design and implementation of Yum-me, and further describe and evaluate two innovative contributions. The rst contriution is an open source state-of-the-art food image analysis model, named FoodDist. We demonstrate FoodDist's superior performance through careful benchmarking and discuss its applicability across a wide array of dietary applications. The second contribution is a novel online learning framework that learns food preference from item-wise and pairwise image comparisons. We evaluate the framework in a eld study of 227 anonymous users and demonstrate that it outperforms other baselines by a signi cant margin. We further conducted an end-to-end validation of the feasibility and e ectiveness of Yum-me through a 60-person user study, in which Yum-me improves the recommendation acceptance rate by 42.63%.