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.
|Published - Sept 2015
|9th ACM Conference on Recommender Systems (RecSys) - Vienna, Austria
Duration: 1 Sept 2015 → …
|9th ACM Conference on Recommender Systems (RecSys)
|1/09/15 → …