FML-Based Recommender System for Restaurants

Due to the increasing number of obese people, this paper presents a Fuzzy Markup Language (FML)-based recommender system for restaurants to infer the recommended level of a restaurant by giving the estimated calories in food, the distance to the restaurant, and the price of the served food. With the standard of the Food Exchange List (FEL), developed by The Japan Diabetes Society, and the information about the food calories, we can estimate the calories of different kinds of the food. We first construct the ontology of this system, and then we use the FML to build the knowledge base and the rule base of the recommender system based on domain experts' suggestions and Japanese persons' experience in diet. With the input of the estimated calories of food, distance of the restaurant, and price of the food, the recommended level of the restaurant is inferred to allow people to both enjoy the delicious food and make an exercise.