Big Data Analytics for the Prediction of Tourist Preferences Worldwide

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Synopsis

(10 chapters)

Prelims

Pages i-xiv

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Introduction

Pages 1-11

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Literature Review

Pages 13-26

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Design of the Proposed System

Pages 27-37

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Predicting Preferences of International and Domestic Tourists Using Association Rule Mining Algorithm

Pages 39-64

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Chapter details

Citation: Padmaja, N., Subramaniam, R. and Mohapatra, S. (2024), "Predicting Preferences of International and Domestic Tourists Using Association Rule Mining Algorithm", Big Data Analytics for the Prediction of Tourist Preferences Worldwide, Emerald Publishing Limited, Leeds, pp. 39-64. https://doi.org/10.1108/978-1-83549-338-020241004

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Predicting Hotel Preferences of International and Domestic Tourists Using Pointwise Mutual Information

Pages 65-81

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Citation: Padmaja, N., Subramaniam, R. and Mohapatra, S. (2024), "Predicting Hotel Preferences of International and Domestic Tourists Using Pointwise Mutual Information", Big Data Analytics for the Prediction of Tourist Preferences Worldwide, Emerald Publishing Limited, Leeds, pp. 65-81. https://doi.org/10.1108/978-1-83549-338-020241005

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Big Data Analytics in Predicting Tourist Preferences Based on Hotel Ratings Using Multiclass Multilabel Classification Algorithm

Pages 83-99

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Citation: Padmaja, N., Subramaniam, R. and Mohapatra, S. (2024), "Big Data Analytics in Predicting Tourist Preferences Based on Hotel Ratings Using Multiclass Multilabel Classification Algorithm", Big Data Analytics for the Prediction of Tourist Preferences Worldwide, Emerald Publishing Limited, Leeds, pp. 83-99. https://doi.org/10.1108/978-1-83549-338-020241006

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Performance Evaluation

Pages 101-108

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Discussion and Conclusion

Pages 109-114

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References

Pages 115-123

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Cover of Big Data Analytics for the Prediction of Tourist Preferences Worldwide

DOI

10.1108/9781835493380

Publication date

2024-02-22

Authors

ISBN

978-1-83549-339-7

eISBN

978-1-83549-338-0

Big Data Analytics for the Prediction of Tourist Preferences Worldwide

Synopsis

Table of contents

Prelims

Introduction

Literature Review

Design of the Proposed System

Predicting Preferences of International and Domestic Tourists Using Association Rule Mining Algorithm

Predicting Hotel Preferences of International and Domestic Tourists Using Pointwise Mutual Information

Big Data Analytics in Predicting Tourist Preferences Based on Hotel Ratings Using Multiclass Multilabel Classification Algorithm

Performance Evaluation

Discussion and Conclusion

References

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