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