: a personalized recipe recommender design based on nutrient information

  • G. Huang

Student thesis: Master


Adopting a healthy diet is difficult for people who lack of nutrient-related knowledge, although it's essential for a healthy lifestyle. In this paper, we propose a recipe recommender system to provide personalized suggestions based on user's nutrient advices. The recommender employs content-based recommendation techniques to dynamically balance the nutrient intake by providing appropriate recipes. Three constraints are utilized to filter out the unhealthy recipes by including user's interaction history with the system and mutual nutrient limitations. We validate our approach with an offline simulation and the evaluation on stability, coverage and efficiency demonstrates that our approach is viable.
Date of Award31 Aug 2013
Original languageEnglish
SupervisorO.D. Amft (Supervisor 1), P. Casale (Supervisor 2), Marc A. Peters (External coach) & J. Hoonhout (External coach)

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