Big data analytics for the prediction of tourist preferences worldwide / by N. Padmaja, Rajalakshmi Subramaniam and Sanjay Mohapatra.

Format:

Book

Author/Creator:

Padmaja, N., author.

Contributor:

Taylor & Francis eBooks

Benjamin Franklin Library Fund.

Series:

Emerald points

Language:

English

Subjects (All):

Big data.

Tourism--Forecasting.

Tourism.

Physical Description:

1 online resource.

Edition:

First edition.

Place of Publication:

Leeds : Emerald Publishing Limited, 2024.

Contents:

Cover

Big Data Analytics for the Prediction of Tourist Preferences Worldwide

Copyright Page

Contents

List of Figures and Tables

List of Abbreviations

Preface

1. Introduction

1.1 Big Data Analytics in Tourism Sector

1.2 Problem Statement

1.3 Objectives

1.4 Research Contributions

1.5 Chapters in the Book

Chapter 1: Introduction

Chapter 2: Literature Review

Chapter 3: Design of the Proposed System

Chapter 4: Predicting Preferences of International and Domestic Tourists Using Association Rule Mining Algorithm

Chapter 5: Predicting Hotel Preferences of International and Domestic Tourists Using Pointwise Mutual Information

Chapter 6: Big Data Analytics in Predicting Tourist Preferences Based on Hotel Ratings Using Multiclass Multilabel Classifi ...

Chapter 7: Performance Evaluation

Chapter 8: Discussion and Conclusion

1.6 Summary

2. Literature Review

2.1 Introduction

2.2 Definition of Big Data Analytics

2.3 Purpose of Big Data Analytics in Tourism Sector

2.4 Benefits of Big Data in Tourism Sector

2.5 Challenges of Big Data in the Tourism Sector

2.6 Application of Big Data in the Tourism Sector

2.7 Research Gap

2.8 Summary

3. Design of the Proposed System

3.1 Introduction

3.2 Description of the Proposed System

Step 1: Data Collection

Step 2: Apply Part of Speech Tagging

Step 3: Estimate Occurrence Frequency

Step 4: Estimate Pointwise Mutual Information (PMI)

Step 5: Generate Output Result

Step 6: Construct a Gold List

Step 7: Vectorized and Labelled

Step 8: Mapping Is Performed

Step 9: Performance Evaluation

Step 10: Compute Accuracy

3.3 Data Set Description

3.4 Implementation of the System

3.5 Summary

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

4.1 Introduction

4.2 Proposed Predicting Preferences of International and Domestic Tourists Using Association Rule Mining System

Step 1: Collect Data

Step 2: Prepare Data

Step 3: Review Data Set Through Association Rule Mining

Support

Confidence

Step 4: Classification and Results

4.3 Discussion and Results

4.3.1 Discussion

4.3.2 Results

4.3.2.1 Domestic City

4.3.2.2 Features of International Cities

4.3.3 Implementation of the Result

4.3.3.1 Features of New Delhi Hotels

4.3.3.2 Features of Beijing Hotels

4.3.3.3 Features of Chicago Hotels

4.3.3.4 Features of Dubai Hotels

4.3.3.5 Features of London Hotels

4.3.3.6 Features of Montreal Hotels

4.3.3.7 Features of New York Hotels

4.3.3.8 Features of San Francisco Hotels

4.3.3.9 Features of Shanghai Hotels

4.3.3.10 International Tourism of Vegas Hotels

4.4 Summary

Notes:

Includes bibliographical references.

Electronic reproduction. London Available via World Wide Web.

Description based on online resource; title from digital title page (viewed on March 26, 2024).

Local Notes:

Acquired for the Penn Libraries with assistance from the Benjamin Franklin Library Fund.

Other Format:

Print version:

ISBN:

9781835493380

1835493386

9781835493403

1835493408

Publisher Number:

40032222042

Access Restriction:

Restricted for use by site license.