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

This chapter shows big data analytics could be used in forecasting the tourist preferences based on the hotel reviews and ratings given by the tourists.

Chahal and Gulia (2016) have referred that big data analytics indicates to the process of examining huge number of information or big data. The big data is gathered from huge set of sources such as videos, social networks, sensors and digital images. The main purpose of big data analytics is to invent new relationships and patterns which might not be visible, and it can offer new insights about users who made it. The technologies of big data explain a new generation of architectures and technologies designed to retrieve value from huge number of data economically by enabling high velocity capture, analysis and/or discovery. According to EMC Education Services (2016), big data helps drive quality, efficiency, personalized services and products, generate developed levels of profit and customer satisfaction. Big data analytics enhances nee avenue of examination with essentially richer outcomes and core insights than available previously. Big data analytics combines unstructured and structured data with real-time queries and feeds opening new ways to insight and innovation. Riahi and Riahi (2018) have stated that big data comprises of a set of numerical information generated by the use of new techniques for professional or personal purposes. Big data analytics is the method of investigating these data in order to uncover market trends, hidden patterns, preferences of customers and other useful data in order to make proper decision. Big data analytics is a rapidly developing technique, and it has been used by several unexpected sectors and has become a big industry player on its own. Big data is regarded as a successor to business intelligence.