Towards Automatic Meal Plan Recommendations for Balanced Nutrition

Food recommenders have been touted as a useful tool to help people achieve a healthy diet. Here we incorporate nutrition into the recommender problem by examining the feasibility of algorithmically creating daily meal plans for a sample of user profiles (n=100), combined with a diverse set of food preference data (n=64) collected in a natural setting. Our analyses demonstrate it is possible to recommend plans for a large percentage of users which meet the guidelines set out by international health agencies. Keywords Health, Recommender Systems, Planning, Prevention

• Type: Journal
• Year: 2015
• Where: RECSYS
• Authors: David Elsweiler, Morgan Harvey
• Keywords: Control Systems, RECSYS 2015