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.
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ISBN-13
978-1-83549-340-3 (9781835493403)
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Schweitzer Klassifikation
Dr. N. Padmaja is Assistant Professor at the Department of Computer Science and Engineering, SRI Padmavati Mahila Visvavidyalayam, India.
Dr. Rajalakshmi Subramaniam is the Founder and CEO at Talaash Research Consultants, Chennai, India.
Dr. Sanjay Mohapatra is Director of Research at Batoi Systems Pvt Ltd, India.
Autor*in
SRI Padmavati Mahila Visvavidyalayam, India
Talaash Research Consultants, India
Batoi Systems Pvt Ltd, India
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
