Preference-maximized nutrition planning by relative learning and ranking.
Effective personal dietary guidelines are essential for health management and prevention of chronic diseases. A key factor toward a successful diet planning is an individual's food preference instead of dogmatic nutrition pattern since it is unlikely that an individual would accept the meal plan merely based on the nutrition supplements. However, the extraction of personal preference is definitely not a trivial matter. The objective of this research is to achieve nutrient-balanced food recommendations for each individual, while considering individual's preferences and requirements at the same time. To reach this goal, we present the k-relative learning technique for semi-automatically extracting users' preferences in a more efficient and effective manner. Comparing to conventional methods, the proposed system can not only reveal users' opinions about foods more fairly but also save lots of labeling efforts during the training data collection stage. In addition, a smarter feedback mechanism is also proposed to enable a more pleasant experience of the user-system interaction. The resulted system is thus expected to improve users' diet habit and compliance with healthier lifestyle.
IBM Research Collaboratory, Taiwan.
Jen-Hao
JH
Hsiao
D001185:Artificial Intelligence; D002908:Chronic Disease / Q000628:therapy*; D004035:Diet Therapy / Q000379:methods*; D005247:Feeding Behavior; D006801:Humans; D015596:Nutrition Assessment; D010347:Patient Care Planning; D057240:Patient Preference; D018802:Patient-Centered Care / Q000379:methods*