Abstract
In this paper a food recommender system giving general and personalized advice is described. The system is based on semi-formal knowledge of personal nutrition gained in a previous project (Food4Me). This project implements a manual expert knowledge based approach using a decision tree for recommending optimal adaptions to the individual nutrition behavior. The system was evaluated in a Proof-of-Principle study whose results lead to the current two research projects. The first project aims at an automated recommender system based on formalized expert knowledge building on the insights from Food4Me. This recommendation system is then extended by including it into an application which provides continuous feedback on the participant's nutritional behavior. Also individual preferences are integrated using approaches from critique based- And persuasive recommender systems. The second system uses an adapted collaborative filtering approach to recommend food based on healthiness and taste ratings of other users augmented by a rating based recommender system for sports. This recommender system is further extended by social support groups and game interaction in view of social motivation. In the future both projects might be used in combination to provide optimal health support.
| Original language | English |
|---|---|
| Pages (from-to) | 21-24 |
| Number of pages | 4 |
| Journal | CEUR Workshop Proceedings |
| Volume | 1533 |
| State | Published - 2015 |
| Event | 2nd International Workshop on Decision Making and Recommender Systems, DMRS 2015 - Bolzano, Italy Duration: 22 Oct 2015 → 23 Oct 2015 |
Keywords
- Collaborative filtering
- Food recommendation
- Personalized recommendation
- Social recommender systems