Applying learning and semantics for personalized food recommendations?

Abstract

We demonstrate the use of a health coach platform that recommends personalized selections of food recipes to diabetic patients. On our platform, we implement a question-answering service that allows questions such as "suggest a good breakfast" to be queried; a response with a list of recipes that is applicable to the patient vis-à-vis their health condition and food preferences is generated. Our research is intended to support the personalization and explainability of recommended food options using a novel application of guideline recommendations encoded in a semantic format. Our platform includes a repository of over half a million recipes and their nutritional content, where each recipe is also represented as a vector-based embedding that has been derived from the recipe's ingredient list and preparation instructions [4].