The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under: Application Areas > Society and Culture Technologies > Machine Learning Application Areas > Business and Industry

中文翻译：

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机器学习应用于旅游业：系统评价


机器学习技术在旅游领域的应用正在经历显着的增长，因为它们可以通过对特定背景下的数据进行智能分析，为该行业中存在的问题提出有效的解决方案。该领域工作的增加需要通过研究活动的定量方法进行详尽的分析，有助于更深入地了解该领域的进展。因此，将分析旅游领域的不同方法，例如规划、预测、建议、预防和安全等。除其他发现外，本次分析的结果表明，监督学习（更具体地说是基于神经网络的技术）在旅游领域的更大影响已得到证实。这项研究的结果不仅使研究人员能够对机器学习在旅游业中的应用有最新、准确的概述，而且还能确定最合适的技术应用于他们感兴趣的领域，以及其他类似的方法来比较他们自己的解决方案。本文分类如下：应用领域 > 社会和文化技术 > 机器学习应用领域 > 商业和工业