Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.19741 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929561052119040 |
|---|---|
| author | Camacho-Ruiz, Miguel Carrasco, Ramón Alberto Fernández-Avilés, Gema LaTorre, Antonio |
| author_facet | Camacho-Ruiz, Miguel Carrasco, Ramón Alberto Fernández-Avilés, Gema LaTorre, Antonio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19741 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tourism destination events classifier based on artificial intelligence techniques Camacho-Ruiz, Miguel Carrasco, Ramón Alberto Fernández-Avilés, Gema LaTorre, Antonio General Finance Artificial Intelligence Information Retrieval Machine Learning 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. |
| title | Tourism destination events classifier based on artificial intelligence techniques |
| topic | General Finance Artificial Intelligence Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2410.19741 |