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Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques

机译:使用多标准和机器学习技术的环保型酒店的顾客分割

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摘要

This study aims to investigate the travellers' choice behaviour towards green hotels through existing online travel reviews on TripAdvisor. Accordingly, a method combining segmentation and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques was developed to segment travellers based on their provided reviews and to prioritize green hotel attributes based on their level of importance in each segment. The data were taken from travellers' online reviews of Malaysian eco-friendly hotels on TripAdvisor. The results showed that the sleep quality was one of the most imporant factors for eco-hotel selection in the majority of segments. The developed method in this study was able to analyse travellers' reviews and ratings on eco-friendly hotels to identify the future choice behaviour and aid travellers in their decision-making process. The study provides new insights for hotel managers and green policy makers on developing environmental-friendly practices.
机译:这项研究旨在通过TripAdvisor的现有在线旅行评论来调查旅行者的选择行为。 因此,基于其提供的评论和基于每个段中的重要性级别的课程,开发了与理想解决方案(TopSIS)技术的相似性与理想解决方案(TOPSIS)技术的相似性偏好的方法的方法和技术 这些数据来自旅行者在TripAdvisor的马来西亚环保型酒店的在线评论。 结果表明,睡眠质量是广大段中环保酒店选择最不风光的因素之一。 该研究中的开发方法能够分析环保型酒店的旅行者评价和评级,以确定在决策过程中的未来选择行为和援助旅行者。 该研究为酒店经理和绿色政策制定者提供了开发环保型实践的新见解。

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