A Personalized Recipe Database System with User- Centered Adaptation and Tutoring Support

Existing online recipe database systems fail to provide support on analyzing and comparing recipes, and practically on adapting existing recipes in order to generate similar dishes that best meet users' needs. In this paper, we present a user-centered adaptive recipe database system called Dish_Master to manage recipes in a novel way, which not only helps users learn basic cooking principles and skills but also provides users with a set of real-time solutions to solve problems that may arise in their cooking practice. A set of modeling constructs is proposed to effectively represent recipe data, constraints, and procedural actions, thereby laying down a foundation for validating properties and assessing the quality of recipes obtained from the Web. Dish_Master also offers facilities to ease the difficulties on identifying the uniqueness and similarity among recipes, which are instrumental to deep mining of recipe data. An experimental prototype system of Dish_Master is developed to demonstrate the feasibility of our approach.

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