通过机器学习技术从报价请求中预测预订者

IF 1.2 Q3 HOSPITALITY, LEISURE, SPORT & TOURISM
Samuel Runggaldier, Gabriele Sottocornola, Andrea Janes, Fabio Stella, M. Zanker
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引用次数: 0

摘要

目的-许多收到的报价请求通常每天都在争夺住宿服务提供者工作人员的注意,而其中一些请求可能比其他请求更值得优先考虑。设计——因此,本研究基于一个大型预订管理系统的通信历史,该系统检查了有抱负的客人的报价请求的特征,以便了解和预测他们的实际预订行为。方法-特别是,我们研究了各种机器学习技术的有效性,通过使用住宿时间、客人数量和类型以及原籍国等特征来预测请求是否会转化为预订。此外,对所涉及的特征进行更深入的分析,以量化它们对预测任务的影响。研究结果——我们的实验评估基于2014年至2019年从意大利南蒂罗尔地区一家四星级酒店收集的大量通信数据集。通过数值实验比较了不同分类模型在数据集上的性能。结果显示,基于我们的方法对提案请求进行优先排序具有潜在的业务优势。此外,很明显,有必要解决类不平衡问题,并正确理解特定于领域的特征,以实现更高的预订类精度/召回率。对特征重要性的调查还显示了信息特征的排名,例如停留时间、请求前的天数以及请求的来源/国家,以便做出准确的预订预测。研究的原创性——据我们所知,这是第一次尝试应用和系统地利用机器学习技术来请求报价数据,以预测请求是否最终会被预订。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BOOKER PREDICTION FROM REQUESTS FOR QUOTATION VIA MACHINE LEARNING TECHNIQUES
Purpose – Many incoming requests for quotation usually compete for the attention of accommodation service provider staff on a daily basis, while some of them might deserve more priority than others. Design – This research is therefore based on the correspondence history of a large booking management system that examines the features of quotation requests from aspiring guests in order to learn and predict their actual booking behavior. Approach – In particular, we investigate the effectiveness of various machine learning techniques for predicting whether a request will turn into a booking by using features such as the length of stay, the number and type of guests, and their country of origin. Furthermore, a deeper analysis of the features involved is performed to quantify their impact on the prediction task. Findings – We based our experimental evaluation on a large dataset of correspondence data collected from 2014 to 2019 from a 4-star hotel in the South Tyrol region of Italy. Numerical experiments were conducted to compare the performance of different classification models against the dataset. The results show a potential business advantage in prioritizing requests for proposals based on our approach. Moreover, it becomes clear that it is necessary to solve the class imbalance problem and develop a proper understanding of the domain-specific features to achieve higher precision/recall for the booking class. The investigation on feature importance also exhibits a ranking of informative features, such as the duration of the stay, the number of days prior to the request, and the source/country of the request, for making accurate booking predictions. Originality of the research – To the best of our knowledge, this is one of the first attempts to apply and systematically harness machine learning techniques to request for quotation data in order to predict whether the request will end up in a booking.
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来源期刊
Tourism and Hospitality Management-Croatia
Tourism and Hospitality Management-Croatia HOSPITALITY, LEISURE, SPORT & TOURISM-
CiteScore
1.90
自引率
23.10%
发文量
33
审稿时长
15 weeks
期刊介绍: Tourism and Hospitality Management is an international, multidisciplinary, open access journal, aiming to promote and enhance research in all fields of the tourism and hospitality industry. It publishes double-blind reviewed papers and encourages an interchange between tourism and hospitality researchers, educators and managers. Editors of Tourism and Hospitality Management strongly promote research integrity and aim to prevent any type of scientific misconduct, such as: fabrication, falsification, plagiarism, redundant publication and authorship problems. All submitted manuscripts are checked using Crossref Similarity Check (iThenticate). Nurturing a scientifically based approach to research, the journal publishes original papers along with empirical research and theoretical articles that contribute to the conceptual development of tourism and hospitality management. Editors look particularly for articles about new trends, challenges and developments, as well as the application of new ideas that are likely to affect the tourism and hospitality industry. The general criteria for the acceptance of articles are: contribution to the scientific knowledge in the field of tourism and hospitality management, scientifically reliable research methodology, relevant literature review and quality of the English language.
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