Tourism destination events classifier based on artificial intelligence techniques

TL;DR

This paper presents an AI-based hierarchical classification system for tourist destination events, enabling consistent, automated cataloging across diverse regions to improve event management and user experience.

Contribution

It introduces a novel automated classification process using machine learning and NLP techniques for diverse tourist events with a hierarchical taxonomy.

Findings

Effective classification of heterogeneous tourist events

Creation of a normalized, cross-regional event catalog

Enhanced support for destination management and user search

Abstract

Identifying client needs to provide optimal services is crucial in tourist destination management. The events held in tourist destinations may help to meet those needs and thus contribute to tourist satisfaction. As with product management, the creation of hierarchical catalogs to classify those events can aid event management. The events that can be found on the internet are listed in dispersed, heterogeneous sources, which makes direct classification a difficult, time-consuming task. The main aim of this work is to create a novel process for automatically classifying an eclectic variety of tourist events using a hierarchical taxonomy, which can be applied to support tourist destination management. Leveraging data science methods such as CRISP-DM, supervised machine learning, and natural language processing techniques, the automatic classification process proposed here allows the creation of a normalized catalog across very different geographical regions. Therefore, we can build catalogs with consistent filters, allowing users to find events regardless of the event categories assigned at source, if any. This is very valuable for companies that offer this kind of information across multiple regions, such as airlines, travel agencies or hotel chains. Ultimately, this tool has the potential to revolutionize the way companies and end users interact with tourist events information.