1 Background
While studies are proliferating on how AI may change the face of tourism [6, 10, 11], there’s an obvious lack of describing which specific AI technologies are in question and how they particularly relate to tourism. This situation risks the use of AI as yet another hollow ‘buzzword’. AI is indeed a game changer for tourism [7] – but we need to specify which technologies we mean by AI, how they work and what they mean for tourism. There are many sub-branches of AI, such as machine learning and deep learning, and delineating them is necessary for maximum utility [12, 13].
While all technologies that aim to mimic human intelligence in non-human platforms can be labeled as Artificial Intelligence (AI) [14, 15], machine learning (ML) refers to a subset of these technologies which comprises software applications that are able to learn to predict outcomes of actions or inputs without human intervention [11]. Machine learning can broadly be categorized into three subsections: supervised, unsupervised, and reinforced learning [16]. Among these categories, multi-layered algorithms that are modeled after the neurons in human brain (artificial neural networks) to make more complex decisions collectively make up the deep learning subfield [17, 18]. Deep learning (DL) is believed to embody the farthest advancement in AI technologies as it creates models that are able to learn from complex environments and make optimal decisions without the need of human input [19, 20]. Therefore, this study focuses on emerging AI-based technologies rooted in deep learning where potential breakthroughs in service provision and customer satisfaction exist.
Anzeige
2 Purpose of the Study
The purpose of this propositional study is to a) identify and review emerging AI-based technologies, chiefly rooted in deep learning, that have a high potential of operational utility for tourism industries, b) propose specific application areas for each identified deep learning technologies within the context of tourism and hospitality.
3 Review of AI-Based Technologies and Application Areas
Upon review of AI, ML and DL literature as well as industry reports, seven AI-based deep learning technologies identified and reviewed in this study were Convolutional Neural Network [20], Style Transfer, Deep Learning Based Recommendation System [21, 22], Generative Adversarial Network, Variational Autoencoder, Recurrent Neural Network and Graph Neural Networks (GNN) [23, 24]. Definition, function, and potential application areas of each technology are summarized in the Table 1.
Table 1.
AI-based DL technologies and potential application areas in tourism
AI-based DL Technology | Type | Generic Application Areas | Application to Tourism |
|---|---|---|---|
Convolutional Neural Network An algorithm specifically designed for image processing and recognition tasks | Computer Vision | Image & facial recognition; medical image analysis; voice recognition; automatic sign language recognition | Secure registration at borders and check-ins, temporary ownership, purchase authorizations |
Style Transfer A computer vision and graphics technique to combine the content of one image with the visual style of another | Computer vision, graphics | Transferring or superimposing famous artistic styles to user-supplied images | Deep localization in marketing communications |
Deep Learning Based Recommender System An application that is based on multiple DL technologies, and uses an algorithm to suggest choices of interest based on Big Data | Ranking | Suggesting consumer products based on past purchases or browsing histories; personalized list of choices; preference predictions (Amazon, Spotify, etc.) | Personalized itineraries, authenticated reviews |
Generative Adversarial Network A type of learning architecture that pits two neural networks against each other to generate new synthetic data in close resemblance to an existing data distribution | Generative AI | Generating examples for image datasets, such as human faces or realistic photographs, image-to-image, text-to-image, or semantic-image-to-photo translations, video prediction (such as Memoji creation in iPhones) | Personalized interactive ads; tangibilization of future experiences |
Variational Autoencoder An algorithm to generate new content while detecting & removing noise | Generative AI | Image morphing; image reconstruction; outlier detection | Event simulations, accuracy in recreational programming |
Recurrent Neural Network An algorithm that operates on sequential data to predict new outcomes | Sequential data processing | Language translation; natural language processing (NLP); speech recognition & image captioning; (e.g., Alexa, Google Translate) | Polylingual tour guiding; universal translators; accessible, anti-ableist visitor experiences |
Graph Neural Networks (GNN) | Solve problems related to graph-structured data | Networks that can be represented via graph. Some examples are social networks, molecular structures, and transportation networks | Tourist flow prediction |
4 Conclusion and Future Implications
This review appraises the most prominent deep learning technologies with applicability to tourism and hospitality industries. Two important highlights of this review were 1) the indispensability of interdisciplinary frameworks to study the utility of AI-based technologies in tourism, and 2) the challenge of reliable data in tourism and hospitality domains. A crucial aspect of AI-based technologies is that they are highly data-dependent. This is a challenge for potential AI applications as sustainable solutions to adequate and accurate data collection are lacking in many tourism sectors. Therefore, it might be necessary to prioritize operational areas that are more conducive to reliable data collection than others such as international border crossings, hotel customer registrations, ticket sales for attractions, etc. AI-based applications may be more likely to succeed if they are first applied in these areas and then expand into other contact areas as reliable data linkages are established.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.