Personalized Clothing and Food Recommendation System Based on Emotions and Weather
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (27)
- DataReportal, "Digital 2021 global digital overview", ~ (2021)
- A. Felfernig, "An overview of recommender systems and machine learning in feature modeling and configuration", 15th International Working Conference on Variability Modelling of Software-Intensive Systems(VaMoS'21), pp.1~8, Association for Computing Machinery
- F. Long, "Wireless Communication and Mobile Computing", pp.1~10 (2020), Hindawi
- J. K. Tarus, "A hybrid recommendation system for e-learning based on context awareness and sequential pattern mining", Soft Computing, 22(8), pp.2449~2461 (2018)
- Phonexay Vilakone, "Movie Recommendation System Based on Users’ Personal Information and Movies Rated Using the Method of k-Clique and Normalized Discounted Cumulative Gain", JIPS(Journal of Information Processing Systems), 16(2), pp.494~507 (2020), 한국정보처리학회[DOI]
- K. Haruna, "Context-aware recommender system : A review of recent developmental process and future research direction", Applied Sciences, 7(12), pp.1211~ (2017)
- 김민정, "상황기반과 협업 필터링 기법을 이용한 개인화 영화 추천 시스템", 정보처리학회논문지. 컴퓨터 및 통신시스템, 4(9), pp.289~296 (2015), 한국정보처리학회[DOI]
- "Twitter"[URL]
- "Facebook"[URL]
- "Instagram"[URL]
- G. S. Park, "Movie recommendation system using SNS data and collaborative filtering algorithm", Master, pp.2017
- K. Sailunaz, "Emotion and sentiment analysis from Twitter text", Journal of Computational Science, 36, pp.1~18 (2019)
- R. Majid, "Conversations sentiment and intent categorization using context RNN for emotion recognition", 7th International Conference on Advanced Computing and Communication Systems (ICACCS), pp.46~50 (2021)
- S. Park, "Emotion recognition from text stories using an emotion embedding model", IEEE International Conference on Big Data and Smart Computing (BigComp), pp.579~583 (2020)
- K. Jia, "Chinese micro-blog sentiment classification based on emotion dictionary and semantic rules", International Conference on Computer Information and Big Data Applications (CIBDA), pp.309~312 (2020)
- Y. Wang, "Emotions extracted from text vs. true emotions–an empirical evaluation in SE context", 34th IEEE, 2019, 230~242
- P. Ekman, "An argument for basic emotions", Cognition And Emotion, 6, pp.169~200 (1992)
- H. Kwak, "What is twitter, a social network or a news media?", Proceedings of the 19th International Conference on World Wide Web, pp.591~600 (2010)
- S. Joshi, "Twitter sentiment analysis system", International Journal of Computer Applications, 180(47), pp.35~39 (2018)
- "표정을 보면 감정을 읽을 수 있을까"[URL]
- T. M. Kim, "A research on the color application method in the product design through the quantification theory of type III to interpret sensibility information", Master, pp.2009
- S. Mohammad, "Emotions evoked by common words and phrases : Using mechanical turk to create an emotion lexicon", Proceedings of the NAACL-HLT 2010Workshop on Computational Approaches to Analysis and Generation of Emotion in Text (2010)
- J. R. L. Bernard, "The Macquarie Thesaurus" (2007), Macquarie Library Pty Ltd
- "기온별 옷차림, 4도~28도까지 기온 및 계절별 옷차림 정보는?…오늘(10일) 자켓⋅야상 필수!"[URL]
- "먹으면 우울한 식품 vs 기분좋은 식품"[URL]
- "슬플 때⋅화날 때⋅우울할 때 먹는 음식? 감정별 상황별 효과 있는‘컴포트 푸드’"[URL]
- "위로를 먹는다, ‘컴포트 푸드’"[URL]