Machine Learning for Smart Tourism and Retail

Abstract

The unprecedented growth in the amount and variety of data we can store about the behaviour of customers has been parallel to the popularization and development of machine learning algorithms. This confluence of factors has created the opportunity of understanding customer behaviour and preferences in ways that were undreamt of in the past. In this chapter, the authors study the possibilities of different state-of-the-art machine learning algorithms for retail and smart tourism applications, which are domains that share common characteristics, such as contextual dependence and the kind of data that can be used to understand customers. They explore how supervised, unsupervised, and recommender systems can be used to profile, segment, and create value for customers.