A Prediction Study on the Localization Policy of Improving the Satisfaction of Chinese Visitors to Japan by Using Matrix Factorization Techniques

作     者：Kaile ZHANG Kenji WATANABE Tomomi AOYAMA 

作者机构：ArchitectureCivil Engineering and Industrial Management EngineeringNagoya Institute of TechnologyNagoya 466-8555Japan 

出 版 物：《Journal of Systems Science and Information》 (系统科学与信息学报（英文）)

年 卷 期：2019年第10卷第6期

页      面：510-531页

核心收录：

学科分类：12[管理学] 120203[管理学-旅游管理] 02[经济学] 1202[管理学-工商管理] 0201[经济学-理论经济学] 020105[经济学-世界经济] 

主　　题：online travel reviews matrix factorization latent factors Chinese visitors to Japan prediction study scenic area 

摘      要：Against the background of the rapid growth of Chinese tourists to Japan,by observing the changes in the patterns of Chinese visitors to Japan and probing into the insufficiency of researches in the past studies,it is believed that online travel reviews can be used to present the current situation of tourism industry and be of value to provide the basic data for long-term sustainable research after analysis,including how to increase Chinese tourists’satisfaction continuously and how to promote the service level of related *** this study,the unknown information caused by the cultural and regional differences in the reviews of Chinese visitors’to Japan is taken as the prediction object and the local tourists’travel comments as a reference ***,the factor decomposition technology and text mining technology are applied to predict the standards of services and projects that can meet the characteristics and demands of Chinese *** comments of Chinese and Japanese tourists who stayed at hotels in the Hakone scenic area from May 2018 to April 2019(time span:one year)are taken as the samples in this ***,the disparity in the purpose of travel between Chinese tourists and Japanese local tourists in this scenic spot can be effectively ***,it also clearly points out that due to a lack of understanding of Japanese food culture,Chinese visitors were in urgent need of relevant services and ***,this method will provide guidance for the growth of the local tourism industry and also demonstrate the feasibility of research *** the end,a complete analysis and calculation method for online comment text mining is established,which also provides guidance on improving the satisfaction of Chinese visitors to ***,this method can also be considered to apply in more tourists with the same cultural background.