Abstract
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.
Original language | English |
---|---|
Publication status | Published - Sept 2015 |
Event | 9th ACM Conference on Recommender Systems (RecSys) - Vienna, Austria Duration: 1 Sept 2015 → … http://recsys.acm.org/recsys15/ |
Conference
Conference | 9th ACM Conference on Recommender Systems (RecSys) |
---|---|
Period | 1/09/15 → … |
Internet address |