Big data analytics for the prediction of tourist preferences worldwide

Big Data analytics and machine learning are being adopted in a range of industries - but how can these technologies be utilised and what can they offer to the tourism industry? In the process of their journeys and in their decision-making processes, people who travel contribute to the generation of...

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Bibliographic Details
Main Authors: Padmaja, N., (Author), Subramaniam, Rajalakshmi, (Author), Mohapatra, Sanjay, (Author)
Format: eBook
Language: English
Published: Bingley, U.K. : Emerald Publishing Limited, 2024.
Series: Emerald points.
Subjects:
Big data.
Tourism > Forecasting.
Business & Economics > Industries > Hospitality, Travel & Tourism.
Hospitality, sports, leisure and tourism industries.
elektronické knihy
electronic books
ISBN: 9781835493403
Physical Description: 1 online resource (144 pages).

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Table of contents

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040 |a UtOrBLW  |b eng  |e rda  |c UtOrBLW 
080 |a 004.6 
100 1 |a Padmaja, N.,  |e author. 
245 1 0 |a Big data analytics for the prediction of tourist preferences worldwide /  |c Dr. N. Padmaja (SRI Padmavati Mahila Visvavidyalayam, India), Dr. Rajalakshmi Subramaniam (Talaash Research Consultants, India), Dr. Sanjay Mohapatra (Batoi Systems Pvt Ltd, India). 
264 1 |a Bingley, U.K. :  |b Emerald Publishing Limited,  |c 2024. 
264 4 |c ©2024 
300 |a 1 online resource (144 pages). 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Emerald points 
500 |a Includes index. 
504 |a Includes bibliographical references. 
505 0 |a 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 Classification Algorithm -- Chapter 7. Performance Evaluation -- Chapter 8. Discussion and Conclusion. 
520 |a Big Data analytics and machine learning are being adopted in a range of industries - but how can these technologies be utilised and what can they offer to the tourism industry? In the process of their journeys and in their decision-making processes, people who travel contribute to the generation of a huge flow of data; all this information is a potential base for creating smart destinations and improving tourism organizations'potential to customize their products and service offerings. The real execution of such inventive forms of data-driven value generation in tourism continues to be more restricted to the theory or used in a few exceptional cases. Big data and machine learning techniques in tourism persists as an unclear concept and a subject of investigation that necessitates closer analysis from an extensive range of field and research methods. Big Data Analytics for the Prediction of Tourist Preferences Worldwide tackles this challenge, exploring the benefits, importance and demonstrates how Big Data can be applied in predicting tourist preferences and delivering tourism services in a customer friendly manner. The authors provide theoretical and experiential contributions designed to see a wider adoption of these technologies in the tourism industry. 
588 0 |a Print version record. 
650 0 |a Big data. 
650 0 |a Tourism  |x Forecasting. 
650 7 |a Business & Economics  |x Industries  |x Hospitality, Travel & Tourism.  |2 bisacsh 
650 7 |a Hospitality, sports, leisure and tourism industries.  |2 thema 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Subramaniam, Rajalakshmi,  |e author. 
700 1 |a Mohapatra, Sanjay,  |e author. 
776 0 8 |i Print version:  |z 9781835493397 
776 0 8 |i PDF version:  |z 9781835493380 
830 0 |a Emerald points. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://doi.org/10.1108/9781835493380  |y Full text 

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