Suitable Hotel Research Articles

Analyzing the impact of COVID-19 vaccination requirements on travelers’ selection of hotels using a fuzzy multi-criteria decision-making approach

In the later stages of the COVID-19 pandemic, hotels are taking various measures to balance pandemic prevention and business operations. Some hotels require travelers to be fully vaccinated prior to check-in, while others do not. In the latter type of hotels, fully vaccinated travelers may encounter others who are not vaccinated. All of these have created constraints for travelers to choose suitable hotel accommodation during this time. To address this issue, a fuzzy multi-criteria decision-making approach is proposed in this study to help traveler choose suitable hotel accommodation. In the proposed methodology, firstly, hotels are divided into two types considering their requirements for COVID-19 vaccination. Travelers are then asked to list the key factors to consider when choosing between these two types of hotels. To derive the priorities of these key factors, the proportionally calibrated fuzzy geometric mean (pcFGM) method is proposed. Subsequently, the fuzzy VIšekriterijumskoKOmpromisnoRangiranje (fuzzy VIKOR) method is applied to evaluate and compare the overall performances of different types of hotels for recommendations to travelers. The applicability of the proposed methodology is illustrated by a real case study. According to the experimental results, most hotels did not request travelers to be full vaccinated. Nevertheless, the hotels recommended to travelers covered both hotel types.

SWARA ve Bulanık COPRAS Yöntemlerini Kullanarak COVID-19 Pandemisinde Güvenli Turizm Sertifikasına Sahip Otel Seçimi

The virus, which emerged in Wuhan, China, has taken the whole world under its influence in a short time. The World Health Organization declared this period as the "pandemic period", which means a global epidemic, due to the spread of the virus to the world in a short time. This period has undoubtedly been a period when many habits changed for everyone. Vacation habit is also one of the habits that change during this period. Especially in the summer months, people who spend their holidays in hotels had to be more sensitive in choosing the hotel due to the continuation of the pandemic period. While different criteria were considered in hotel selection before, the criterion of having a safe tourism certificate has become the most important criterion due to the pandemic. Businesses with this certificate can provide service without any problems as they take all precautions during the pandemic period. In this study, a hotel selection problem that takes into account the criteria of safe tourism certificate is discussed. In this study, a hotel selection problem that takes into account the safe tourism certification criteria is discussed. In the study, Stepwise Weight Assessment Ratio Analysis (SWARA) and fuzzy logic based The Complex Proportional Assessment (COPRAS) methods were used to solve the problem of choosing the most suitable hotel. As a result of the study, ten hotel alternatives were evaluated according to the criteria of safe tourism certificate during the pandemic period and the most suitable hotel was selected.

A fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach for suitable hotel recommendation amid the COVID-19 pandemic.

Cities around the world have reopened from the lockdown caused by the COVID-19 pandemic, and more and more people are planning regional travel. Therefore, it is a practical problem to recommend suitable hotels to travelers amid the COVID-19 pandemic. However, it is also a challenging task since the critical factors that affect hotel selection amid the COVID-19 pandemic may be different from those usually considered. From this perspective, the fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach is proposed in this study for hotel recommendation. The proposed methodology not only considers the critical factors affecting hotel selection amid the COVID-19 pandemic, but also establishes a systematic mechanism, that is, enhanced fuzzy geometric mean, to simultaneously improve the accuracy and efficiency of the recommendation process. The fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach has been successfully applied to recommend suitable hotels to 10 travelers for regional trips amid the COVID-19 pandemic.

An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews

Finding a suitable hotel based on user’s need and affordability is a complex decision-making process. Nowadays, the availability of an ample amount of online reviews made by the customers helps us in this regard. This very fact gives us a promising research direction in the field of tourism called hotel recommendation system which also helps in improving the information processing of consumers. Real-world reviews may showcase different sentiments of the customers towards a hotel and each review can be categorized based on different aspects such as cleanliness, value, service, etc. Keeping these facts in mind, in the present work, we have proposed a hotel recommendation system using Sentiment Analysis of the hotel reviews, and aspect-based review categorization which works on the queries given by a user. Furthermore, we have provided a new rich and diverse dataset of online hotel reviews crawled from Tripadvisor.com. We have followed a systematic approach which first uses an ensemble of a binary classification called Bidirectional Encoder Representations from Transformers (BERT) model with three phases for positive–negative, neutral–negative, neutral–positive sentiments merged using a weight assigning protocol. We have then fed these pre-trained word embeddings generated by the BERT models along with other different textual features such as word vectors generated by Word2vec, TF–IDF of frequent words, subjectivity score, etc. to a Random Forest classifier. After that, we have also grouped the reviews into different categories using an approach that involves fuzzy logic and cosine similarity. Finally, we have created a recommender system by the aforementioned frameworks. Our model has achieved a Macro F1-score of 84% and test accuracy of 92.36% in the classification of sentiment polarities. Also, the results of the categorized reviews have formed compact clusters. The results are quite promising and much better compared to state-of-the-art models. The relevant codes and notebooks can be found here.

An Approach Towards Hotel Recommender System

Recommendation Systems (RS) suggest the right item to the right user. It predicts the user’s rating to an item and based on this rating RS provides the suggestion to users. In today’s world many online applications are already using the Recommendation system that provides a recommendation for a particular item like books, movies, music etc. in an automated fashion. This paper proposed a system that helps to find the best suitable hotel in a given geographical area according to the user query by using library “recommenderlab” in R. This study proposed a system that gives the best hotel available according to the user rating available in database. User makes their decision according to their recommendation provides by the proposed system for finding best suitable hotel from available database and shows on the map by using a leaflet map package.

The selection model for horizontal alliances between hotels and airlines: an integrated application of NGT, fuzzy TOPSIS and MCGP methods

El modelo de selección Para alianzas horizontales entre hoteles y aerolíneas (Haa): una aplicación integrada de los métodos NGT, fuzzy TOPSIS y MCGPObjetivoEl objetivo principal de este documento es evaluar el problema de selección de hoteles de las aerolíneas para su HAA (hotel airline alliance) a fin de desarrollar clientes potenciales para las aerolíneas. Este documento propondrá un modelo híbrido de evaluación matemática para ayudar a la aerolínea a seleccionar un hotel óptimo con criterios cualitativos y cuantitativos.Diseño/metodología/enfoquePara resolver el problema de selección de hoteles de las aerolíneas para su HAA, este documento se centra en la implementación de los modelos NGT, Fuzzy TOPSIS y MCGP en el proceso de selección de hoteles. Inicialmente, el NGT se utilizó para crear criterios de toma de decisiones de HAA basados en la revisión de la literatura y las opiniones de expertos, y se descubrió que la escala y la posibilidad de elección, el valor de la marca, la atracción turística, los costes operativos y las condiciones industriales son los criterios más importantes. Posteriormente, se utilizó el método Fuzzy TOPSIS para obtener la preferencia fuzzy general y normalizada y calcular los coeficientes de cercanía de cada hotel alternativo con respecto a cada criterio. En tercer lugar, se incorporaron cinco restricciones tangibles al modelo Fuzzy TOPSIS-MCGP para calcular el hotel óptimo con el software LINGO.ResultadosLos gerentes de aerolíneas pueden usar el modelo propuesto para tener una visión clara de cómo elegir el hotel más adecuado para colaborar con el fin de superar a sus competidores. Tener acceso a esta información permite a los gerentes de las aerolíneas tomar medidas para gestionar mejor y mejorar el resultado de la alianza, lo que les ayuda a ganar más confianza en su capacidad de toma de decisiones y al mismo tiempo reducir los riesgos de inversión.Originalidad/valorEste es el primer documento que adopta el modelo Fuzzy TOPSIS-MCGP para seleccionar un hotel para su HAA desde el punto de vista de la aerolínea. Las principales contribuciones de este estudio son las siguientes: 1. Se propone un marco de evaluación eficiente y simple para manejar la imprecisión y la incertidumbre en los problemas de selección de hoteles del mundo real. 2. El avance del tratamiento de la incertidumbre en el proceso MCDM; extiende el método fuzzy TOPSIS-MCGP a tales problemas, teniendo en cuenta criterios tangibles e intangibles. 3. Los gerentes de aerolíneas ahora pueden tomar decisiones al elegir el mejor hotel para su HAA que cumpla con los objetivos comerciales de la aerolínea y las demandas de los pasajeros. 4. Los operadores de hoteles son flexibles en la selección de su asociación de aerolíneas, creando así mayores ganancias para ambas partes.Palabras clave:Hotel, Aerolínea, Técnica de grupo nominal (NGT), Técnica Para el orden de preferencia por similitud a solución real (TOPSIS), Programación de objetivos de opción múltiple (MCGP), Alianza horizontalTipo de papelTrabajo de investigación

An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

In recent times, selection of a suitable hotel location and reservation of accommodation have become a critical issue for the travelers. The online hotel search has been increased at a very fast pace and became very time-consuming due to the presence of huge amount of online information. Recommender systems (RSs) are getting importance due to their significance in making decisions and providing detailed information about the required product or a service. To acquire the hotel recommendations while dealing with textual hotel reviews, numerical ranks, votes, ratings, and number of video views have become difficult. To generate true recommendations, we have proposed an intelligent approach which also deals with large-sized heterogeneous data to fulfill the needs of the potential customers. The collaborative filtering (CF) approach is one of the most popular techniques of the RS to generate recommendations. We have proposed a novel CF recommendation approach in which opinion-based sentiment analysis is used to achieve hotel feature matrix by polarity identification. Our approach combines lexical analysis, syntax analysis, and semantic analysis to understand sentiment towards hotel features and the profiling of guest type (solo, family, couple etc). The proposed system recommends hotels based on the hotel features and guest type for personalized recommendation. The developed system not only has the ability to handle heterogeneous data using big data Hadoop platform but it also recommends hotel class based on guest type using fuzzy rules. Different experiments are performed over the real-world datasets obtained from two hotel websites. Moreover, the values of precision and recall and F-measure have been calculated, and the results are discussed in terms of improved accuracy and response time, significantly better than the traditional approaches.

Hotel recommendation system by bipartite networks and link prediction

With the rapid growth of the Internet in recent years, online social media has become very important for people. People often use social media tools to communicate and share their ideas or experiences regardless of time and place. One of the areas where the use of these tools is widespread is tourism. It is one of the hardest tasks to find a suitable hotel for travellers. There are many websites where accommodation services of the tourism enterprises are evaluated. People share their experiences about the hotels they stayed through these websites. Positive or negative comments are an effective factor in hotel selection. The purpose of this study is to construct a hotel recommendation system based on user’s location using online hotel reviews. The reviews crawled from TripAdvisor.com were filtered according to their total scores, and a dataset was obtained consisting of users and reviews of their liked hotels. For the recommendation task, first, three different bipartite networks consisting of users and hotels were modelled, which were global, country and city based. Then, in these networks, it was recommended hotels to users for their next choice with link prediction methods, using the common hotels where the users prefer to stay in the past. The most successful results were obtained in the city-based network. With this study, it was tried to reduce the time spent reading the online reviews and finding the suitable hotel. To the best of our knowledge, this is the first study on recommending a hotel using online reviews by bipartite networks.

Quality of hotel services – selected issues

The paper presents problems connected with quality relationship between hotels and theirs clients. The article discusses the most important aspects of the quality of hotel services. Method: Descriptive method. Basic conditions of the development of contemporary tourism is how to provide a suitable hotel product to clients witch wants consume a perfect tourism service. The research into consumption in world tourism shows that prices, quality of products and the condition of the natural environment are playing, and in the future will play, a decisive role in the choice of destinations by tourists. Quality is a very important element of each product: good and service. Hotels must concentrate on many elements for creating high level of quality. The quality of hotel services can be discussed, on the one hand, from the point of view of the service provider as a standard of service based on experience and forced externally, that is - a collection of characteristics that a given service should possess in order to fully satisfy the needs of a hotel guest with regard to accommodation and food. On the other hand, the quality of services is perceived individually by the customers, who define it as the difference between what they expect and a subjective judgment of what they receive.

Cloud decision support model for selecting hotels on TripAdvisor.com with probabilistic linguistic information

Hotel selection on tourism websites has been a prevailing trend in traveling in recent years, and selecting a suitable hotel is a high-risk decision for millions of tourists because of the intrinsic imperceptibility of the provided products. Decision support models for hotel selection have drawn the interest of numerous scholars. The problem is that some existing models cannot address substantial amounts of online information, and they neglect the influence of interrelationships among different criteria on tourists’ decisions. To cover these defects, an applicable hotel decision support model is developed for tourists utilizing online reviews on TripAdvisor.com. Considering a great deal of review information associated with hotels posted by numerous tourists on TripAdvisor.com, probabilistic linguistic term sets (PLTSs) are introduced to summarize this information statistically. Processing qualitative concepts requires effective support of reliable tools; then, a cloud model can be employed to deal with probabilistic linguistic information. First, PLTSs are converted, and a novel concept of probabilistic linguistic integrated cloud (PLIC) is proposed. Moreover, the essential algorithms and distance measure of PLICs are defined. Two information fusion tools are subsequently presented based on Heronian mean operator. Then, the hotel decision support model is established. Finally, a hotel selection problem on TripAdvisor.com is provided to demonstrate the model. Its stability and validity are further verified by a sensitivity analysis and comparison with other extant methods.

Game theoretic approach of a novel decision policy for customers based on big data

In recent days, big data based analysis in hotel industry become popular. Merchants are attracting clients using the accurate analysis of historic data and predicting the behavior of possible clients to perform proper marketing strategy. To study the principle of the game between clients and merchants, in this work, we propose a novel two-stage game theoretic approach of decision policy for clients when choosing the suitable hotel to stay among many candidates, the merchants will provide a non-cooperative game strategy to attract the attention of potential clients. Analysis of the non-cooperative game method based on big data has been given. Simulation results indicate that, by using our proposed novel method, the average price for clients to choose a satisfied hotel is reduced and the successful rate of stay is increased for merchants, which will bring the expected income to a higher level because of the sticky phenomena of users.

Budgeting practices: a study on Brazilian hotel companies

This study aims to explore the budgeting practices in the hotel companies of Brazil’s main tourist centers. The justification is oriented by the number of foreign tourists received by the top ten Brazilian tourist destinations, which reaches 95% of the country. Another motivation is the lack of research on hotel companies in developing countries, and it should also be considered that Brazil will host the World Cup in 2014 and the Olympic Games in 2016, which requires a suitable hotel structure to receive a large number of athletes and tourists. Thus, in order to achieve the purpose of this study, surveys were sent to the hotels associated with the Brazilian Association of the Hotel Industry (ABIH), with replies by 40 hotel companies. Descriptive statistics were applied to the results, in order to improve the basis for discussions. The results indicate that most of the hotels adopt corporate budgeting with bottom-up as main budgeting process. Research shows that budgets are developed, more frequently, using the results from previous years and economic indicators. In the performance evaluation, the comparative analysis, the analysis of the causes of deviations and adequacy measures are often applied. The analysis of variance is primarily directed at controlling costs/expenses, identifying potential problems and providing feedback for the next period.

Interval-Valued Intuitionistic Fuzzy Multicriteria Group Decision Making Approach for Hotel Selection

Evaluating and selecting the most suitable hotel location for development is complex and challenging. To effectively deal with this problem, this paper presents an interval-valued intuitionistic fuzzy multicriteria group decision making approach for evaluating and selecting hotel locations. The subjectiveness and imprecision of the decision making process are adequately modeled by the use of interval-valued based intuitionistic fuzzy numbers. The concept of ideal solutions is adopted for determining the overall performance of each alternative hotel location across all the selection criteria on which the final decision is made. An example is presented for demonstrating the applicability of the proposed approach for solving real world hotel location selection problems.

Sem-Fit: A semantic based expert system to provide recommendations in the tourism domain

The hotel industry is one of the leading stakeholders in the tourism sector. In order to reduce the traveler’s cost of seeking accommodations, enforce the return ratio efficiency of guest rooms and enhance total operating performance, evaluating and selecting a suitable hotel location has become one of the most critical issues for the hotel industry. In this scenario, recommender services are increasingly emerging which employ intelligent agents and artificial intelligence to “cut through” unlimited information and obtain personalized solutions. Taking this assumption into account, this paper presents Sem-Fit, a semantic hotel recommendation expert system, based on the consumer’s experience about recommendation provided by the system. Sem-Fit uses the consumer’s experience point of view in order to apply fuzzy logic techniques to relating customer and hotel characteristics, represented by means of domain ontologies and affect grids. After receiving a recommendation, the customer provides a valuation about the recommendation generated by the system. Based on these valuations, the rules of the system are updated in order to adjust the new recommendations to past user experiences. To test the validity of Sem-Fit, the validation accomplished includes the interaction of the customer with the system and then the results are compared with the expert recommendation for each customer profile. Moreover, the values of precision and recall and F1 have been calculated, based on three points of view, to measure the degree of relevance of the recommendations of the fuzzy system, showing that the system recommendations are on the same level as an expert in the domain.

Enhancing Hotel Search with Semantic Web Technologies

Tourism service providers are more and more under pressure to offer products of greater complexity and diversity to meet the ever-changing demands of travelers; the individualistic consumption patterns and lifestyles makes it increasingly difficult for tourist service providers to anticipate behavior and configure their services accordingly, i.e. the tourist industry must focus more on a hybrid consumer whose travel choice will be more complex. Although current online travel systems aim to support the customer in finding a suitable hotel or even a whole trip, most of the work is still up to the customer, who has to consider several sources of information before deciding which hotel to book. Furthermore, since the quality of a hotel room w.r.t the requirements of the end-user are multi-dimensional and cannot be easily expressed on discrete scales, the main critical issue in such cases is a price/benefit ratio which is defined by what is known, as the best booking. To tackle these problems an advanced search technology that considers the ratio and ranks results accordingly to the user requirements is needed. In this paper we propose a framework which uses Semantic Web technologies for an improved exploration and rating of hotels for business customers in order to reduce the search time and costs, which, in turn, results in a huge benefit for the end-users. The framework provides methods for modeling domain specific expert knowledge and integration of diverse heterogeneous data sources. Semantic technologies enable business customer to formalize their requirements and to combine those requirements with aggregated hotel information like location or features, thus achieving a selection of the hotels ranked according to the customer's requirements.