International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Deep Learning-based Tourism Recommendation System using Social Network Analysis

  • Jeong, Chi-Seo (Department of Information System KwangWoon University Graduate School of Smart Convergence) ;
  • Ryu, Ki-Hwan (Department of Tourism Industry, Graduate school of smart convergence, Kwangwoon University) ;
  • Lee, Jong-Yong (Ingenium College of liberal arts, Kwangwoon University) ;
  • Jung, Kye-Dong (Ingenium College of liberal arts, Kwangwoon University)
  • Received : 2020.04.02
  • Accepted : 2020.04.13
  • Published : 2020.05.31
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Abstract

Numerous tourist-related data produced on the Internet contain not only simple tourist information but also diverse ideas and opinions from users. In order to derive meaningful information about tourist sites from such big data, the social network analysis of tourist keywords can identify the frequency of keywords and the relationship between keywords. Thus, it is possible to make recommendations more suitable for users by utilizing the clear recommendation criteria of tourist attractions and the relationship between tourist attractions. In this paper, a recommendation system was designed based on tourist site information through big data social network analysis. Based on user personality information, the types of tourism suitable for users are classified through deep learning and the network analysis among tourist keywords is conducted to identify the relationship between tourist attractions belonging to the type of tourism. Tour information for related tourist attractions shown on SNS and blogs will be recommended through tagging.

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References

  1. Baker, P., and Gourley, B. (2014). Data divination: big data strategies. Delmar Learning.
  2. Kaisler, S., Armour, F., Espinosa, J. A., and Money, W. (2013, January). Big data: Issues and challenges moving forward. In 2013 46th Hawaii International Conference on System Sciences (pp. 995-1004). IEEE. DOI: https://doi.org/10.1109/hicss.2013.645
  3. Dubey, G., Rana, A., and Shukla, N. K. (2015, February). User reviews data analysis using opinion mining on web. In 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE) (pp. 603-612). IEEE. DOI: https://doi.org/10.1109/ABLAZE.2015.7154934
  4. Kim, H.S, Park, M.S and Kang, P.S (2016). Analysis of Deep Learning Research Trends through Topic Modeling and Social Networks. Journal of the Spring Conference of the Korean Industrial Engineering Association, 1877-1899.
  5. Wu, X., Zhu, X., Wu, G. Q., and Ding, W. (2013). Data mining with big data. In transactions on knowledge and data engineering, 26(1), 97-107. IEEE. DOI: https://doi.org/10.1109/TKDE.2013.109
  6. Russom, P. (2011). Big data analytics. In best practices report, fourth quarter, 19(4), 1-34. TDWI.
  7. Seo, J.A, and Oh, I.G (2017). Analysis of Daegu's Tourism Image Using Social Network Analysis: Focusing on Online Big Data. Journal of the Korea Creative Content Association, 17(7), 443-454. DOI: https://doi.org/10.5392/JKCA.2017.17.07.443
  8. Scott, J. (1988). Social network analysis. Sociology, 22(1), 109-127. https://doi.org/10.1177/0038038588022001007
  9. Minami, T., & Baba, K. (2014). An Attempt to Find Potential Group of Patrons from Library's Loan Records. International Journal of Internet, Broadcasting and Communication, 6(1), 5-8. IJIBC. DOI: https://doi.org/10.7236/IJIBC.2014.6.1.5
  10. Jeong, C. S., Lee, J. Y., & Jung, K. D. (2020). Adaptive Recommendation System for Tourism by Personality Type Using Deep Learning. International Journal of Internet, Broadcasting and Communication, 12(1), 55-60. IJIBC. DOI: https://doi.org/10.7236/IJIBC.2020.12.1.55