RT Journal Article
T1 Tourism destination events classifier based on artificial intelligence techniques
A1 Camacho Ruiz, Miguel
A1 Carrasco González, Ramón Alberto
A1 Carrasco González, Ramón Alberto
A1 Fernández Avilés, Gema
A1 Latorre, Antonio
AB 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.
PB Elsevier
YR 2023
FD 2023-10-05
LK https://hdl.handle.net/20.500.14352/103261
UL https://hdl.handle.net/20.500.14352/103261
LA eng
NO Camacho-Ruiz, M., Carrasco, R. A., Fernández-Avilés, G., & LaTorre, A. (2023). Tourism destination events classifier based on artificial intelligence techniques. Applied Soft Computing, 148, 110914. https://doi.org/10.1016/j.asoc.2023.110914
DS Docta Complutense
RD 25 dic 2025