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Case Study of Big Data-Based Agri-food Recommendation System According to Types of Customers
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 Title & Authors
Case Study of Big Data-Based Agri-food Recommendation System According to Types of Customers
Moon, Junghoon; Jang, Ikhoon; Choe, Young Chan; Kim, Jin Gyo; Bock, Gene;
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The Korea Agency of Education, Promotion and Information Service in Food, Agriculture, Forestry and Fisheries launched a public data portal service in January 2015. The service provides customized information for consumers through an agri-food recommendation system built-in portal service. The recommendation system has fallowing characteristics. First, the system can increase recommendation accuracy by using a wide variety of agri-food related data, including SNS opinion mining, consumer's purchase data, climate data, and wholesale price data. Second, the system uses segmentation method based on consumer's lifestyle and megatrends factors to overcome the cold start problem. Third, the system recommends agri-foods to users reflecting various preference contextual factors by using recommendation algorithm, dirichlet-multinomial distribution. In addition, the system provides diverse information related to recommended agri-foods to increase interest in agri-food of service users.
Agri-food;Recommendation System;Types of Consumer;Food-Related Lifestyle;Big data Analysis;
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