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This paper aims to find a way to personalize attraction recommendations for travelers. The research objective is to find a more accurate way to suggest new attractions to each traveler based on the opinions of other like-minded travelers and the traveler’s preferences.

To achieve the goal, developers have created a personalized system to generate attraction recommendations. The system considers an individual traveler’s preferences to construct a qualitative attraction ranking model. The new ranking model is the result of blending two processes: K-means clustering and the analytic hierarchy process (AHP).

The performance of the developed recommendation system has been assessed by measuring the accuracy and scalability of the ranking model of the system. The experimental results indicate that the ranking model always returns accurate results independent of the number of attractions and the number of travelers in each cluster. The ranking model has also proved to be scalable because the processing time is independent of the numbers of travelers. Additionally, the results reveal that the overall system usability is at a very satisfactory level.

The main theoretical implication is that integrating the processes of K-means and AHP techniques enables a new qualitative ranking model for personalized recommendations that deliver only high-quality attractions. However, the designed recommendation system has some limitations. First, it is necessary to manually update information about the new tourist attractions. Second, the overall response time depends on the internet bandwidth and latency.

This research contributes to the tourism business and individual travelers by introducing an accurate and scalable way to suggest new attractions to each traveler. The potential benefit includes possible increased revenue for travel agencies that offer personalized package tours and support individual travelers to make the final travel decisions. The designed system could also integrate with itinerary planning systems to plot out a journey that pinpoints what travelers will most enjoy.

This research proposes a design and implementation of a personalized recommendation system based on the qualitative attraction ranking model introduced in this article. The novel ranking model is designed and developed by integrating K-means and AHP techniques, which has proved to be accurate and scalable.

本研究主要探索如何建立个性化旅游胜地推荐模型。本研究通过分析旅游兴趣相似的游客意见和游客偏好选择, 建立一种更加准确推荐游客需要的旅游胜地方法。

为了达到研究目的, 本研究建立了一种个性化推荐旅游胜地的信息系统。其系统通过分析每个游客的旅游偏好来建设一种定性旅游胜地排名模型。这种新型模型主要通过结合以下两种分析算法：（1）K平均聚类算法（K-means clustering）（2）层次分析法（AHP）。

本研究建立的推荐信息系统经过了准确率和拓展性的测评。实验结果表明这种排名模型的准确率并不受旅游胜地多少和游客样本大小的影响。此外, 这种排名模型也具有拓展性, 因为算法时间并不受游客样本大小的影响。最后, 研究实验表明此排名模型客户体验性达到合格满意要求。

本研究的主要理论意义在于其结合了K平均聚类算法和层次分析法, 并建立了一种新型定性排名模型, 这种排名模型个性化地推荐更高质量的旅游胜地给游客。然而, 这种推荐信息系统有一些局限性。第一, 新旅游胜地的信息需要手动输入。第二, 整个系统的处理时间决定于网络带宽和延迟状况。

本研究的实践意义在于其建立了一种准确和具有拓展性的新型旅游胜地推荐模型。这种模型的潜在价值将有利于旅游机构提供定制化旅游套餐和帮助游客制定旅游计划。此外, 这种模型还可以结合旅游路线计划系统以制定更加使游客满意的旅游行程。

本研究推荐了一种基于定性旅游胜地排名模型的个性化旅游推荐模型。这种新型的排名模型结合K平均聚类算法和层次分析法, 实验证明这种模型更具准确性和拓展性。

This study aims to develop a framework to identify and prioritize the key factors in automation and artificial intelligence (AI) implementation in the hospitality and tourism industry.

This paper used the analytic hierarchy process, a multi-criteria decision-making method, to prioritize the factors influencing automation and AI implementation. This paper developed a model with five criteria (human knowledge, services, robotics applications, internal environment and institutional environment) and 23 sub-criteria obtained from previous studies. This paper designed a questionnaire in the form of pairwise comparisons based on the proposed hierarchical structure. This paper used a nine-point ranking scale to show the relative significance of each variable in the hierarchy and tested the model among staff from 35 five-star hotels and top-rated tourism agencies in the United Arab Emirates.

Human knowledge, services and robotics applications were the most significant factors influencing automation and AI implementation. Practitioners and researchers in the hospitality and tourism industry could apply the proposed framework to develop sustainable strategies for implementing and managing automation and AI. The proposed framework may also be useful in future studies examining AI implementation in the hospitality and tourism industry.

This paper developed a framework for policymakers that identifies and could help to overcome some of the challenges in implementing automation and AI in the hospitality and tourism sector around the world. The results provide an agenda for future research in this area.

酒店业和旅游业中的自动化和人工智能

摘要

本研究旨在开发一个框架, 以确定和优先考虑酒店业和旅游业自动化和人工智能（AI）实施的关键因素。

我们使用层次分析法, 一种多准则决策方法, 对影响自动化和人工智能实施的因素进行优先排序。我们建立了一个包含五个标准（人类知识、服务、机器人技术应用、内部环境和制度环境）的模型, 并从先前的研究中获得了23个子标准。基于所提出的层次结构, 我们设计了一份以成对比较的形式进行的问卷调查。我们使用九点评分量表来说明每个标准或子标准在层级中的相对重要性, 并在阿联酋35家五星级酒店和顶级旅游机构的员工中测试了该模型。

人类知识、服务和机器人应用是影响自动化和人工智能实施的最重要因素。酒店业和旅游业的从业人员和研究人员可以应用拟议的框架来制定实施和管理自动化和人工智能的可持续战略。拟议的框架也可能有助于未来研究人工智能在酒店业和旅游业的实施。

我们为决策者开发了一个框架, 用来识别并帮助其克服全球酒店业和旅游业实施自动化和人工智能方面的一些挑战。研究结果为今后该领域的研究提供了一个议程。

Automatización e inteligencia artificial en hostelería y turismo

Resumen

Este estudio tuvo como objetivo desarrollar un marco para identificar y priorizar los factores clave en la implementación de la automatización y la inteligencia artificial (IA) en la industria hotelera y turística.

utilizamos el proceso de jerarquía analítica, un método de toma de decisiones de varios criterios, para priorizar los factores que influyen en la automatización y la implementación de la IA. Desarrollamos un modelo con cinco criterios (conocimiento humano, servicios, aplicaciones robóticas, entorno interno y entorno institucional) y 23 subcriterios obtenidos de estudios previos. Diseñamos un cuestionario en forma de comparaciones por pares basados en la estructura jerárquica propuesta. Usamos una escala de clasificación de nueve puntos para mostrar la importancia relativa de cada variable en la jerarquía y probamos el modelo entre el personal de 35 hoteles de cinco estrellas y las agencias de turismo mejor calificadas en los Emiratos Árabes Unidos.

el conocimiento humano, los servicios y las aplicaciones robóticas fueron los factores más importantes que influyeron en la automatización y la implementación de la inteligencia artificial. Los profesionales e investigadores de la industria hotelera y turística podrían aplicar el marco propuesto para desarrollar estrategias sostenibles para implementar y gestionar la automatización y la IA. El marco propuesto también puede ser útil en estudios futuros que examinen la implementación de la IA en la industria hotelera y turística.

desarrollamos un marco para los formuladores de políticas que identifica y podría ayudar a superar algunos de los desafíos en la implementación de la automatización y la inteligencia artificial en el sector de la hospitalidad y el turismo en todo el mundo. Los resultados proporcionan una agenda para futuras investigaciones en esta área.