US researchers have recently created an AI system that can recommend recipes tailored around the preferences and dietary needs of individual users.
"Our work focuses on personalized food recommendation. In particular, given a user query in natural language, we want to retrieve the top matches in a recipe dataset," said Mohammed J. Zaki, one of the researchers who developed the system called "pFoodReQ".
The system, developed at the Rensselaer Polytechnic Institute and IBM Research in New York, aims to help people find healthy recipes that satisfy both their dietary needs and inclinations, and at the same time, prevent them from some components that may cause harm in certain health conditions such as carbohydrates and sugary foods for diabetes patients, and some types of fruit or nuts for allergic people.
"The key idea is that given the same query, the response should actually be different for different users. This is a very challenging task, especially in terms of determining the implicit constraints that are actually relevant to the query," Zaki told the TechXplore website.
The pFoodReQ dataset contains over 67 million records, in addition to graph representations of the relationships between these recipes and the ingredients required to complete them, as well as data related to the properties of ingredients, nutrient contents, and nutritive value.
