Prediction of tourist behaviour: Tourist visiting places by adapting convolutional long short-Term deep learning

Jaruwan Kanjanasupawan, Yi Cheng Chen, Tipajin Thaipisutikul, Timothy K. Shih, Anongnart Srivihok

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The trend of industry tourism GDP is increasing in every year that speculates from statistics of the World Travel Tourism Council (2018). Moreover, travel industry not only considered as the most dynamic sector but also the most importance generator of income and jobs in the country. Thus, the prototype for tourism plans are needed for strategic planning. Currently, social web is a great tool for providing useful insights about tourist behaviors especially with the text data that comes from travelers' opinions. In this work, we use sequential patterns of users' behavior which are ordered by time from tourist including opinions, reviews as our input data. Then, we use Convolutional Long Short-Term Deep Learning (CLSTDL) which is a deep learning technique that combines Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) to predict the expected location. During the process, the output of CNN will be fed into LSTM to learn the sequence pattern behavior of traveler. The model output is then used to predict the next location that particular travelers are likely to go. The experimental results have shown that CLSTDL outperforms other models when evaluating with the accuracy and loss metrics.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on System Science and Engineering, ICSSE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages12-17
Number of pages6
ISBN (Electronic)9781728105253
DOIs
StatePublished - Jul 2019
Event2019 International Conference on System Science and Engineering, ICSSE 2019 - Dong Hoi City, Quang Binh Province, Viet Nam
Duration: 20 Jul 201921 Jul 2019

Publication series

NameProceedings of 2019 International Conference on System Science and Engineering, ICSSE 2019

Conference

Conference2019 International Conference on System Science and Engineering, ICSSE 2019
Country/TerritoryViet Nam
CityDong Hoi City, Quang Binh Province
Period20/07/1921/07/19

Keywords

  • CLSTDL
  • CNN
  • LSTM
  • sequence learning Convolutional Long Short-Term Deep Learning

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