There are a lot of services and mobile applications that allow simplifying a search, proactively providing information about famous attractions and user feedback; but travelers may have difficulty in choosing based on their real needs. Smart tourism, under the rapid growth of Internet of Things and machine learning techniques is developed for enhancing travellers’ experience and satisfaction. In recent years, it is essential for most tourism industrialists to strengthen their competitive edge and to improve industrial sustainability through the adoption of smart tourism. In this chapter, the proposed model generates travel recommendations and related useful information to end users through an online platform, namely Niche-E-Travel (NET). This distinctive tourism solution aims to collect all the obscure attractions, to align them with visitors’ interests, and to provide them with a new to-do list in Hong Kong. NET collects basic information from end users and uses the proposed travel analytic model with K-modes and K-means clustering methods to finish a clustering process, and provide some potential activity plans to fit the end user’s interests and requirements. Recommendations made for each user are supported by collaborative filtering to compare different users’ personal interests.
Part of the book: Tourism