A Consumer Recommendation System based on Big Data

Abstract

The recommendation system is a system to make consumers' choice easier by informing the result of combining information necessary for individuals in the information that consumers want in the market. Ultimately, it is a program to increase the satisfaction of consumers. In this study, consumers who choose cosmetics combine the existing attributes and extract the characteristics, and then recommend similar cosmetics, thereby enhancing the availability of consumers. For this purpose, the cosmetics classification of 'hwahae App', a representative cosmetics application in Korea, was used and 'recommendation system based on similarity algorithm' was developed. This study conducted a previous study on the algorithms that form the type and recommendation system of the recommendation system, and then developed a customized cosmetics recommendation system based on Big Data. The suggestion of such a recommended algorithm can help increase consumer satisfaction by receiving similar products that are suitable for their skin type or taste within the consumer market where there are numerous products in the market today. As a result, the company will be able to reduce the cost and time of purchasing cosmetics while increasing satisfaction by being recommended for other products with similar characteristics to existing ones. In conclusion, the application of the recommendation system using big data is meaningful in that it has not only practicality but also academic meaning by utilizing big data algorithms.