Ji-Won Baek1, Joo-Chang Kim1, Junchul Chun2, Kyungyong Chung2. 1. Data Mining Lab, Department of Computer Science, Kyonggi University, Suwon-si 16227, Korea. 2. Division of Computer Science and Engineering, Kyonggi University, Suwon-si 16227, Korea.
Abstract
BACKGROUND: Humans supply a variety of nutrients to their body in dietary life, which are directly related to health. Chronic diseases are long accumulated in the body on account of heredity or living habits, and draw attention as a main issue in the era of disease-controlled longevity. Therefore, it is essential to make health care continuously through the improvement in dietary habits. OBJECTIVE: By recommending alternative food products whose diet and nutrition structure is similar to that of the food products positively influencing users' health conditions, it is possible to satisfy user's health and preference. METHOD: We used the hybrid clustering based food recommendation method that uses chronic disease based clustering, diet and nutrition ontology, diet and nutrition knowledge base. Active users are classified into the chronic disease based cluster that has the nearest euclidean distance. According to the classified clusters, food products are recommended to users, and similar food products are also recommended with the use of food clustering and knowledge base. Food products are clustered with the uses of k-means algorithm and food and nutrient data system. Based on the created food clusters and food preference data, diet and nutrition knowledge base is generated. It is composed of food cluster filter, food similarity filter, universal preference filter, and user feedback filter. The universal preference filter represents the similarity weight between diet and nutrition, and user preference. The user feedback filter has the similarity weight between active user preference and diet and nutrition. They continue to be updated through associated feedback. RESULT: The proposed health decision-making method takes into account each user's health condition so that the method has more precision than an existing recommendation method. In addition, the proposed method brings about better evaluation results than a general user-by-user health context information based recommendation method. CONCLUSION: By recommending the food products related to users' chronic diseases through the proposed hybrid clustering, it is possible to help out their healthcare. In addition, by letting users receive satisfying feedback flexibly, it is possible to improve their dietary habits.
BACKGROUND:Humans supply a variety of nutrients to their body in dietary life, which are directly related to health. Chronic diseases are long accumulated in the body on account of heredity or living habits, and draw attention as a main issue in the era of disease-controlled longevity. Therefore, it is essential to make health care continuously through the improvement in dietary habits. OBJECTIVE: By recommending alternative food products whose diet and nutrition structure is similar to that of the food products positively influencing users' health conditions, it is possible to satisfy user's health and preference. METHOD: We used the hybrid clustering based food recommendation method that uses chronic disease based clustering, diet and nutrition ontology, diet and nutrition knowledge base. Active users are classified into the chronic disease based cluster that has the nearest euclidean distance. According to the classified clusters, food products are recommended to users, and similar food products are also recommended with the use of food clustering and knowledge base. Food products are clustered with the uses of k-means algorithm and food and nutrient data system. Based on the created food clusters and food preference data, diet and nutrition knowledge base is generated. It is composed of food cluster filter, food similarity filter, universal preference filter, and user feedback filter. The universal preference filter represents the similarity weight between diet and nutrition, and user preference. The user feedback filter has the similarity weight between active user preference and diet and nutrition. They continue to be updated through associated feedback. RESULT: The proposed health decision-making method takes into account each user's health condition so that the method has more precision than an existing recommendation method. In addition, the proposed method brings about better evaluation results than a general user-by-user health context information based recommendation method. CONCLUSION: By recommending the food products related to users' chronic diseases through the proposed hybrid clustering, it is possible to help out their healthcare. In addition, by letting users receive satisfying feedback flexibly, it is possible to improve their dietary habits.