Big Data Analytics for the Prediction of Tourist Preferences Worldwide (Emerald Points) -- Digital download

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Big Data Analytics for the Prediction of Tourist Preferences Worldwide (Emerald Points) -- Digital download

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  • 商品コード 9781835493380

Full Description

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.

Contents

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

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