Forecasting Mode of Sports Tourism Demand Based on Support Vector Machine

Zhang, Fan; Jiang, Qinhua; Zhu-ying, Wang

doi:10.1145/3338188.3338219

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Another use of computational intelligent algorithms in eCRM is to forecast the tourism demand [18][19][20][21], while other researchers use it for the classification of tourist scenarios [22,23].…”

Section: Related Workmentioning

confidence: 99%

Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence

et al. 2021

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In the tourism industry it is common that the information obtained from customers can be varied, dispersed, and with high volumes of data. In this context, the automatic analysis of information has been proposed through electronic customer relationship management, which refers to marketing activities, tools and techniques, delivered with the use of electronic channels for the specific purpose of locating, building and improving long- term relationships with customers, to enhance their individual potential. In this paper, we refer to the analysis of information in three aspects: customer satisfaction, the study of customer behavior and the forecast of tourist demand. Specifically, we have created a novel dataset comprising the non-verbal preference assessment of tourists who are clients of the Sol Cayo Guillermo hotel belonging to the Melia hotel chain, in Jardines del Rey, Cuba. Then, by applying Computational Intelligence algorithms to this dataset, we achieve segment customers according to their non-verbal preferences, in order to increase their satisfaction, and therefore the client profitability. In order to achieve a good performance in the realization of this task, we have proposed two modifications of the Naïve Associative Classifier, whose results are compared with the most relevant computational algorithms of the state of the art. The experimentally obtained values of balanced accuracy and averaged F1 measure show that, by clearly improving the results of the state-of-the-art algorithms, our proposal is adequate to successfully use electronic customer relationship management in the tourist services provided by hotel chains.

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Section: Related Workmentioning

confidence: 99%

Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence

et al. 2021

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“…We feel there are common shocks to all four accommodation categories, and VARMA models allow us to utilize information across the different categories. BPNN and SVM are chosen because they are two of the most commonly used machine learning algorithms, and have previously been successfully applied to tourism demand forecasting (Silva, Hassani, Heravi, & Huang, 2019;Zhang, Jiang, & Wang, 2019). LSTM is included because it represents the current state of art in machine learning.…”

Section: Modelsmentioning

confidence: 99%

Multi‐horizon accommodation demand forecasting: A New Zealand case study

Zhu

Wang

2020

Journal of Tourism Research

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This paper contributes to the filling of two gaps in accommodation demand forecasting: (a) the limited number of studies on the use of modern machine learning techniques to identify the dynamics of accommodation demand; and (b) the lack of understanding of comparative forecasting performance of different modelling techniques at multiple forecast horizons. We show that, as the forecast horizon increases, the performance of machine learning is stable and robust. We also find that the long short-term memory has particular advantages in long-horizon forecasting and handling data with complex structure in New Zealand.

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“…Zhang and collaborators have also investigated the forecast of the demand for sports tourism [82], while other researchers have done so in the classification of tourist scenarios [83,84].…”

Section: Automatic Data Analysis and Its Application To Tourism Ecrmmentioning

confidence: 99%

Application of Data Engineering in Automatic information analysis for Electronic Customer Relationship Management: A Survey

2020

IJETER

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In this paper we address Customer Relationship Management and electronic Customer Relationship Management from the perspective of the confluence of two relevant areas of engineering: Behavioral Engineering and Data Engineering. After conducting extensive documentary research on the state of the art,we review some of the most recent works of automatic data analysis applied to the tourism industry, considering the particularities of Behavioral Engineering within the systemic paradigm. As a result of this research work, one of the most important conclusions we have reached is the following: we determine that, to obtain an efficient electronic Customer Relationship Management model in a tourism related organization, the use of Intelligent Computing techniques for automatic data analysis is needed to ostensibly improve working conditions related to the tourism industry.

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Forecasting Mode of Sports Tourism Demand Based on Support Vector Machine

Cited by 6 publications

References 4 publications

Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence

Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence

Multi‐horizon accommodation demand forecasting: A New Zealand case study

Application of Data Engineering in Automatic information analysis for Electronic Customer Relationship Management: A Survey

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