Forecasting hotel room prices in selected GCC cities using deep learning

Shehhi, Mohammed Al; Karathanasopoulos, Andreas

doi:10.1016/j.jhtm.2019.11.003

Cited by 41 publications

(24 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For G2, 39.5% is excellent and 34.7% is good. For G3, 22.7% is excellent and 40.5% is good, which is different from the result obtained by Al Shehhi and Karathanasopoulos ( 2020 ). This may be due to the differences in the development priorities of towns with different culture–tourism characteristics.…”

Section: Discussioncontrasting

confidence: 93%

Exploration of Social Benefits for Tourism Performing Arts Industrialization in Culture–Tourism Integration Based on Deep Learning and Artificial Intelligence Technology

Zhang

2021

Front. Psychol.

View full text Add to dashboard Cite

As a product of the tourism performing arts industry in culture–tourism integration development, to develop a featured culture–tourism town is a new trend for tourism development in the new era. To analyze the social benefit of the culture–tourism industry, in this study, an artificial intelligence model for social benefit evaluation is constructed based on backpropagation (BP) neural network and fuzzy comprehensive analysis, with Yiyang Town taken as an example. The criterion layer in the model includes three indexes (life benefit G1, environmental benefit G2, and economic benefit G3), and the index layer contains 11 indexes (H1–H11). The weight values of cultural inheritance and protection, ecological environment improvement, and commercial economy development to the social benefit of the town are 0.522, 0.570, and 0.424, respectively. For G1, 41.20% is excellent; for G2, 39.5% is excellent; and for G3, 40.5% is good. In general, 30.76% of the total social benefit is excellent, with 37.69% being good, 21.48% being qualified, and 10.07% being unqualified. It is inferred that the total social benefit level of Yiyang Town is good according to the constructed model. Therefore, the culture inheritance and protection, the ecological environment improvement, and the commercial economy development are the key evaluation factors of social benefit.

show abstract

Section: Discussioncontrasting

confidence: 93%

Exploration of Social Benefits for Tourism Performing Arts Industrialization in Culture–Tourism Integration Based on Deep Learning and Artificial Intelligence Technology

Zhang

2021

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…Recommender systems in tourism, an example of AI, have been the focus of research in recent years (Nilashi et al, 2017). Another recent application of ML models was to forecast hotel room prices in the Gulf Cooperation Council (GCC) countries (Al Shehhi & Karathanasopoulos, 2020). ML was used as a technique to predict travellers' choice preferences of eco-friendly hotels (Nilashi et al, 2019) and as a means for searching query data for tourism and hospitality forecasting .…”

Section: The Complexity Of Human Behaviour (Peb)mentioning

confidence: 99%

Application of machine learning to predict visitors’ green behavior in marine protected areas: evidence from Cyprus

Rezapouraghdam¹,

Akshiq

Ramkissoon

2021

Journal of Sustainable Tourism

View full text Add to dashboard Cite

Interpretive marine turtle tours in Cyprus yields an alluring ground to unfold the complex nature of pro-environmental behavior among travelers in nature-based destinations. Framing on Collins (2004) interaction ritual concept and the complexity theory, the current study proposes a configurational model and probes the interactional effect of visitors' memorable experiences with environmental passion and their demographics to identify the causal recipes leading to travelers' sustainable behaviors. Data was collected from tourists in the marine protected areas located in Cyprus. Such destinations are highly valuable not only for their function as an economic source for locals but also as a significant habitat for biodiversity preservation. Using fuzzy-set Qualitative Comparative Analysis (fsQCA), this empirical study revealed that three recipes predict the high score level of visitors' environmentally friendly behavior. Additionally, an adaptive neuro-fuzzy inference system (ANFIS) method was applied to train and test the patterns of visitors' proenvironmental behavior in a machine learning environment to come up with a model which can best predict the outcome variable. The unprecedented implications on the use of technology to simulate and encourage pro-environmental behaviors in sensitive protected areas are discussed accordingly.

show abstract

“…(Ba, Nguyen, Thang, Le, & Huynh, 2019) The online review patterns of users from developed and developing countries are different. (Zhang, 2019) Machine learning approaches outperform traditional models (Karaoglan, Temizkan, & Findik, 2019) Conducted sentimental analysis of hotels reviews (Al Shehhi & Karathanasopoulos, 2020) Machine learning performance is far better than the seasonal autoregressive integrated moving average (SARIMA) model. (Chang, Ku, & Chen, 2020) Analyzed hotel reviews (Sánchez-Medina & Eleazar, 2020) Predicted hotel booking and cancellation using ANN 86 | P a g e https://jaauth.journals.ekb.eg/…”

Section: P a G Ementioning

confidence: 99%

“…Table 2 shows the different areas covered by machine learning in the hotel industry after analyzing the existing literature. (Zhao, Dong, & Yang, 2015), Gradient Boosting is more effective than SVM (Arruza, Pericich, & Straka, 2016), and machine learning algorithms outperform Seasonal Autoregressive Integrated Moving Average (SARIMA) (Al Shehhi & Karathanasopoulos, 2020). Table 3 shows the machine learning algorithms used in the hotel industry.…”

Section: Rq1: Where Does the Hotel Industry Implement Machine Learning?mentioning

confidence: 99%

“…Yang, Tang, Luo, & Law, 2015a), robotic room services (P.-Y. Yang, Chang, Chang, & Wu, 2018), hotel maintenance prediction (Athuraliya & Farook, 2018), , separating real and fake online reviews (Antonio, de Almeida, Nunes, Batista, & Ribeiro, 2018), and predicting hotel energy demand (Casteleiro-Roca et al, 2018), predicting booking cancellation (Antonio, de Almeida, & Nunes, 2019), hotel room prices forecasting (Al Shehhi & Karathanasopoulos, 2020;Antonio, de Almeida, & Nunes, 2017;Lee & Lee, 2020). Many qualitative studies have also been reported such as discussion on artificial intelligence in the hotel industry (Ananeva, 2019;J.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Machine Learning in the Hotel Industry: A Critical Review

Alotaibi

2020

Journal of Association of Arab Universities for Tourism and Hos

View full text Add to dashboard Cite

Study purpose-The hotel industry like any other industry is witnessing a change due to information and communication technology. However, this change is quite slow. Many researchers in recent time have garnered interest in exploring and implementing the new technologies of artificial intelligence and machine learning in the hotel industry. Therefore, the purpose of this study is to give insights on the role of ML and its integrated technologies in the hotel industry. Design/Methodology/Approach-The study has critically reviewed articles published from 2010 to 2020. To achieve the research objective, the study seeks to answer three main research questions related to the existing literature; RQ1: Where does the hotel industry implement machine learning? RQ2: What are the machine learning techniques used in the hotel industry? RQ3: Which countries are using machine learning in the hotel industry? Findings-The study found that machine learning is helpful in demand forecasting, price forecasting, booking cancellation prediction, financial efficiency, and work efficiency. The machine learning algorithms outperform in the forecast accuracy against the statistical models. The countries at the forefront in machine learning technologies are China and USA. The other countries should take the cue from them and implement machine learning in their hotels Originality of the research-This research conducts exploratory analysis to identify the extent of scientific community knowledge and awareness on machine learning in the hotel industry. To the best of the authors' knowledge, no prior researcher has conducted a similar study specifically in the hotel industry.

show abstract

Forecasting hotel room prices in selected GCC cities using deep learning

Cited by 41 publications

References 46 publications

Exploration of Social Benefits for Tourism Performing Arts Industrialization in Culture–Tourism Integration Based on Deep Learning and Artificial Intelligence Technology

Exploration of Social Benefits for Tourism Performing Arts Industrialization in Culture–Tourism Integration Based on Deep Learning and Artificial Intelligence Technology

Application of machine learning to predict visitors’ green behavior in marine protected areas: evidence from Cyprus

Application of Machine Learning in the Hotel Industry: A Critical Review

Contact Info

Product

Resources

About