• Title/Summary/Keyword: Constructing Tourist Destination

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Constructing Attractive Tourist Destination - Case of Japanese 'Top 100 Tourist Destination' Contents - (매력있는 관광지 만들기 - 일본의 '관광지 만들기 100선' 콘텐츠를 중심으로 -)

  • Lim, Eun-Mi;Choi, In-Ho
    • The Journal of the Korea Contents Association
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    • v.8 no.6
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    • pp.253-260
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    • 2008
  • Korean local governments attempt to propose various ideas for regional development, and Korean central government give support to balanced regional development. Construction tourist destination is the most important item in regional development policy of Korean central government. Because, tourism leads to human and material exchange. tourism contents is more concerned. The purpose of this paper will be to provide the suggestion for constructing tourist destination. To accomplish this, contents of attractive tourist destination was analysed through Japanese case study of constructing tourist destination.

Multi-dimensional Analysis and Prediction Model for Tourist Satisfaction

  • Shrestha, Deepanjal;Wenan, Tan;Gaudel, Bijay;Rajkarnikar, Neesha;Jeong, Seung Ryul
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.2
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    • pp.480-502
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    • 2022
  • This work assesses the degree of satisfaction tourists receive as final recipients in a tourism destination based on the fact that satisfied tourists can make a significant contribution to the growth and continuous improvement of a tourism business. The work considers Pokhara, the tourism capital of Nepal as a prefecture of study. A stratified sampling methodology with open-ended survey questions is used as a primary source of data for a sample size of 1019 for both international and domestic tourists. The data collected through a survey is processed using a data mining tool to perform multi-dimensional analysis to discover information patterns and visualize clusters. Further, supervised machine learning algorithms, kNN, Decision tree, Support vector machine, Random forest, Neural network, Naive Bayes, and Gradient boost are used to develop models for training and prediction purposes for the survey data. To find the best model for prediction purposes, different performance matrices are used to evaluate a model for performance, accuracy, and robustness. The best model is used in constructing a learning-enabled model for predicting tourists as satisfied, neutral, and unsatisfied visitors. This work is very important for tourism business personnel, government agencies, and tourism stakeholders to find information on tourist satisfaction and factors that influence it. Though this work was carried out for Pokhara city of Nepal, the study is equally relevant to any other tourism destination of similar nature.