No abstract

With the improvement of people's living standards, the public has become more and more concerned about healthy diet. A healthy diet is the first line of defense for human health. The existing recommendation services on catering mostly consider personal preference and usually ignore the people's demand for healthy diet. To solve such problem, we consider the healthy diet recommendation system with balanced healthiness and personalization, where the online and offline interactions of food and beverage are more contextualized and diversified, and the personalized needs of users are context sensitive. In particular, we study the process and system implementation of healthy diet recommendation, and consider the actual user needs of healthy diet recommendation system. By integrating two algorithms of knowledge filtering and collaborative filtering by process, we design a modular system architecture framework and realize the acquisition of contextual information and ontology module with rule-based inference.