Personalized Clothing and Food Recommendation System Based on Emotions and Weather

Abstract

In the era of the 4th industrial revolution, we are living in a flood of information. It is very difficult and complicated to find theinformation people need in such an environment. Therefore, in the flood of information, a recommendation system is essential. Amongthese recommendation systems, many studies have been conducted on each recommendation system for movies, music, food, and clothes. To date, most personalized recommendation systems have recommended clothes, books, or movies by checking individual tendenciessuch as age, genre, region, and gender. Future generations will want to be recommended clothes, books, and movies at once by checkingage, genre, region, and gender. In this paper, we propose a recommendation system that recommends personalized clothes and foodat once according to the user's emotions and weather. We obtained user data from Twitter of social media and analyzed this data asuser's basic emotion according to Paul Eckman’s theory. The basic emotions obtained in this way were converted into colors by applyingHayashi's Quantification Method III, and these colors were expressed as recommended clothes colors. Also, the type of clothing isrecommended using the weather information of the visualcrossing.com API. In addition, various foods are recommended according tothe contents of comfort food according to emotions.