Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Guerra-Montenegro, Juan; Sánchez-Medina, Javier J.; Laña, Ibai; Sánchez-Rodríguez, David; Alonso-González, Itziar; Ser, Javier Del

doi:10.1016/j.asoc.2021.107082

Cited by 30 publications

(8 citation statements)

References 166 publications

(171 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first general reference on a solution to the imbalance phenomenon is the work by Chawla et al (2002), where a methodology based on the creation of synthetic samples is used to balance the dataset at hand. Regarding the literature on the treatment of imbalanced data in the tourism sector using machine learning techniques, the review paper by Guerra-Montenegro et al (2021) shows that, up to that date, only a remarkable paper was found: Li and Sun (2012) focused on firm failure prediction. In such work, nearest neighbour techniques jointly with a Support Vector Machine (SVM) approach are used to generate additional samples of the minority class.…”

Section: H1mentioning

confidence: 99%

An empirical study on the imbalance phenomenon of data from recommendation questionnaires in the tourism sector

Duque

Fernández-Muñóz

Moguerza

et al. 2023

JTF

View full text Add to dashboard Cite

PurposeRecommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.Design/methodology/approachAn opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).FindingsThrough the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.Originality/valueThe main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

show abstract

Section: H1mentioning

confidence: 99%

An empirical study on the imbalance phenomenon of data from recommendation questionnaires in the tourism sector

Duque

Fernández-Muñóz

Moguerza

et al. 2023

JTF

View full text Add to dashboard Cite

show abstract

“…The articles ( Li et al, 2021 ; Yu, Wang & Lai, 2008 ) demonstrated that timescale decomposition is an actual efficient method that followed the “divide-and-conquer” approach. For example, the divide-and-conquer approach has been used in several fields: oil prices ( Rădulescu et al, 2020 ; Wang et al, 2018 ) foreign currency exchange rate ( Jin et al, 2021 ; Lin, Chiu & Lin, 2012 ; Wang & Luo, 2021 ), stock market trend ( Cheng & Wei, 2014 ; Na & Kim, 2021 ; Stasiak, 2020 ; Wang & Luo, 2021 ), wind speed ( Hu et al, 2021 ; Wang et al, 2014 ; Xie et al, 2021 ), electronics sales ( Chen & Lu, 2021 ; Lu & Shao, 2012 ), healthcare ( Aileni, Rodica & Valderrama, 2016 ; Dwivedi et al, 2019 ; Singh, Dwivedi & Srivastava, 2020 ), and tourism market ( Chen, Lai & Yeh, 2012 ; Guerra-Montenegro et al, 2021 ; Tang et al, 2021 ). The hybrid EMD combined with the artificial neural network(ANN) method was applied to predict the first, second, and third steps moving forward wind speed time series ( Chen et al, 2021 ; Hu et al, 2021 ; Liu et al, 2012 ; Liu, Hara & Kita, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical mode decomposition using deep learning model for financial market forecasting

Jin

Jin²,

Chen

2022

PeerJ Computer Science

View full text Add to dashboard Cite

Financial market forecasting is an essential component of financial systems; however, predicting financial market trends is a challenging job due to noisy and non-stationary information. Deep learning is renowned for bringing out excellent abstract features from the huge volume of raw data without depending on prior knowledge, which is potentially fascinating in forecasting financial transactions. This article aims to propose a deep learning model that autonomously mines the statistical rules of data and guides the financial market transactions based on empirical mode decomposition (EMD) with back-propagation neural networks (BPNN). Through the characteristic time scale of data, the intrinsic wave pattern was obtained and then decomposed. Financial market transaction data were analyzed, optimized using PSO, and predicted. Combining the nonlinear and non-stationary financial time series can improve prediction accuracy. The predictive model of deep learning, based on the analysis of the massive financial trading data, can forecast the future trend of financial market price, forming a trading signal when particular confidence is satisfied. The empirical results show that the EMD-based deep learning model has an excellent predicting performance.

show abstract

“…So far, recommendation software has been integrated with various industries, providing personalized recommendations that enhance user experiences and drive customer satisfaction [8]. The hospitality sector, specifically the hotel industry, has recognized the importance of computational intelligence, and recommender systems in particular, in delivering tailored recommendations to guests, thereby improving their overall stay [9].…”

Section: Introductionmentioning

confidence: 99%

Using ChatGPT and Persuasive Technology for Personalized Recommendation Messages in Hotel Upselling

Remountakis,

Kotis,

Kourtzis

et al. 2023

Information

View full text Add to dashboard Cite

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity, and personalization, recommender systems can effectively influence user decision making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present pilot experiments with a case study involving a hotel recommender system. Our inhouse commercial hotel marketing platform, eXclusivi, was extended with a new software module working with ChatGPT prompts and persuasive ads created for its recommendations. In particular, we developed an intelligent advertisement (ad) copy generation tool for the hotel marketing platform. The proposed approach allows for the hotel team to target all guests in their language, leveraging the integration with the hotel’s reservation system. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between ChatGPT and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.

show abstract

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Cited by 30 publications

References 166 publications

An empirical study on the imbalance phenomenon of data from recommendation questionnaires in the tourism sector

An empirical study on the imbalance phenomenon of data from recommendation questionnaires in the tourism sector

Empirical mode decomposition using deep learning model for financial market forecasting

Using ChatGPT and Persuasive Technology for Personalized Recommendation Messages in Hotel Upselling

Contact Info

Product

Resources

About