Preference-driven personalized recommendation by k-comparative annotation and reasoning

Good eating habits are important for maintaining a healthy life and preventing the lifestyle-related disease epidemic. Researches about menu recommendation or diet planning are thus attracting much attention recently. 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. In this paper, we present the k-comparative annotation and reasoning 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 food annotation efforts during the training data collection stage. The resulted system is thus expected to improve users' diet habit and compliance with healthier lifestyle.