KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Personalized Clothing and Food Recommendation System Based on Emotions and Weather

감정과 날씨에 따른 개인 맞춤형 옷 및 음식 추천 시스템

  • ;
  • 박두순 (순천향대학교 컴퓨터소프트웨어공학과)
  • Received : 2021.12.30
  • Accepted : 2022.03.14
  • Published : 2022.11.30
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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 the information people need in such an environment. Therefore, in the flood of information, a recommendation system is essential. Among these 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 tendencies such as age, genre, region, and gender. Future generations will want to be recommended clothes, books, and movies at once by checking age, genre, region, and gender. In this paper, we propose a recommendation system that recommends personalized clothes and food at once according to the user's emotions and weather. We obtained user data from Twitter of social media and analyzed this data as user's basic emotion according to Paul Eckman's theory. The basic emotions obtained in this way were converted into colors by applying Hayashi's Quantification Method III, and these colors were expressed as recommended clothes colors. Also, the type of clothing is recommended using the weather information of the visualcrossing.com API. In addition, various foods are recommended according to the contents of comfort food according to emotions.

4차 산업혁명 시대를 맞아 우리는 정보의 홍수 속에 살고 있다. 이런 환경에서 우리에게 필요한 정보를 찾기란 매우 어렵고 복잡하다. 따라서 정보의 홍수 속에서 추천 시스템은 필수적이다. 이러한 추천 시스템 중 영화, 음악, 음식, 의류의 각각에 대한 추천 시스템들은 많은 연구가 진행되어 왔다. 현재까지 대부분의 개인화 추천 시스템들은 개인의 성향인 나이, 장르, 지역, 성별 등을 체크해서 옷들을 추천한다던가, 책들을 추천한다던가, 영화들을 추천해왔다. 미래 세대에서는 나이, 장르, 지역, 성별 등을 체크해서 옷, 책, 영화들을 한꺼번에 추천 받기를 원할 것이다. 본 논문에서는 사용자의 감정과 날씨에 따라 개인 맞춤형 옷과 음식을 한꺼번에 추천하는 추천 시스템을 제안한다. 소셜미디어인 트위터에서 사용자의 데이터를 얻었고, 트윗을 기반으로 감정 분석을 해서 Paul Eckman 이론에 따라 사람의 6 가지의 기본 감정으로 분류했다. 이렇게 얻어진 기본 감정을 Hayashi의 Quantification Method III를 적용하여 색깔로 변환하였으며, 이러한 색깔은 추천하는 옷의 색상으로 표현하였다. 또한, visualcrossing.com API의 날씨 정보를 이용하여 의류의 종류를 추천한다. 그리고 감정에 따른 컴포트 푸드의 내용에 따라 다양한 음식을 추천한다.

Keywords

Acknowledgement

This research was supported by the National Research Foundation of Korea (No. NRF-2020R1A2B5B01002134) and BK21 FOUR (No.5199990914048).

References

  1. DataReportal, "Digital 2021 global digital overview," 2021.
  2. A. Felfernig, V. M. Le, A. Popescu, M. Uta, T. N. T. Tran, and M. Atas, "An overview of recommender systems and machine learning in feature modeling and configuration," In 15th International Working Conference on Variability Modelling of Software-Intensive Systems(VaMoS'21), Association for Computing Machinery, New York, NY, USA, Article 16, pp.1-8.
  3. F. Long, "Improved personalized recommendation algorithm based on context-aware in mobile computing environment," Wireless Communication and Mobile Computing, Hindawi, pp.1-10, 2020.
  4. J. K. Tarus, Z. Niu, and D. Kalui, "A hybrid recommendation system for e-learning based on context awareness and sequential pattern mining," Soft Computing, Vol.22, No.8, pp.2449-2461, 2018. https://doi.org/10.1007/s00500-017-2720-6
  5. P. Vilakone, K. Xinchang, and D. S. Park, "Movie recommendation system based on users' personal information and movies rated using the method of k-clique and normalized discounted cumulative gain," Journal of Information Processing Systems, Vol.16, No.2, pp.494-507, 2020. https://doi.org/10.3745/JIPS.04.0169
  6. K. Haruna et al., "Context-aware recommender system: A review of recent developmental process and future research direction," Applied Sciences, Vol.7, No.12, pp.1211, 2017. https://doi.org/10.3390/app7121211
  7. M. J. Kim, D. S. Park, M. Hong, and H. M. Lee, "Personalized movie recommendation system using context-aware collaborative filtering technique," KIPS Transactions on Computer and Communication Systems, Vol.4, No.9, pp.289-296, 2015. https://doi.org/10.3745/KTCCS.2015.4.9.289
  8. Twitter [Internet], http://twitter.com.
  9. Facebook [Internet], http://facebook.com.
  10. Instagram [Internet], https://www.instagram.com.
  11. G. S. Park, "Movie recommendation system using SNS data and collaborative filtering algorithm," Chonnam National University Master's thesis, 2017.
  12. K. Sailunaz and R. Alhajj, "Emotion and sentiment analysis from Twitter text," Journal of Computational Science, Vol. 36, pp.1-18, 2019.
  13. R. Majid and H. A. Santoso, "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.
  14. S. Park, B. Bae, and Y. Cheong, "Emotion recognition from text stories using an emotion embedding model," IEEE International Conference on Big Data and Smart Computing (BigComp), pp.579-583, 2020.
  15. K. Jia and Z. Li, "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.
  16. Y. Wang, "Emotions extracted from text vs. true emotions-an empirical evaluation in SE context," 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp.230-242, 2019.
  17. P. Ekman, "An argument for basic emotions," Cognition And Emotion, Vol.6, pp.169-200, 1992. https://doi.org/10.1080/02699939208411068
  18. H. Kwak, C. Lee, H. Park, and S. Moon, "What is twitter, a social network or a news media?," Proceedings of the 19th International Conference on World Wide Web, ACM, April, pp.591-600, 2010.
  19. S. Joshi and D. Deshpande, "Twitter sentiment analysis system," International Journal of Computer Applications, Vol.180, No.47, pp.35-39, 2018.
  20. 표정을 보면 감정을 읽을 수 있을까 [Internet], https://www.dong ascience.com/news.php?idx=35206
  21. 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's thesis, The University of Seoul, 2009.
  22. S. Mohammad and P. Turney, "Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon," Proceedings of the NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, LA, California, 2010.
  23. J. R. L. Bernard, "The Macquarie Thesaurus," Australia, Macquarie Library Pty Ltd, 2007.
  24. 기온별 옷차림, 4도~28도까지 기온 및 계절별 옷차림 정보는?... 오늘(10일) 자켓.야상 필수! [Internet], http://www.kyeongin.com/main/ view.php?key=20181010000733388
  25. 먹으면 우울한 식품 vs 기분좋은 식품 [Internet], http://www.mindgil.com/news/articleView.html?idxno=68844
  26. 슬플 때.화날 때.우울할 때 먹는 음식? 감정별 상황별 효과 있는 '컴포트 푸드' [Internet], https://m.blog.naver.com/PostView.naver?isHttpsRedirect=true&blogId=kfcc_no1& logNo=221079601681
  27. 위로를 먹는다, '컴포트 푸드' [Internet], https://www.foodnews.news/news/article.html?no=216465