Destination Recommendation Research Articles

Group conflict and group harmony affect future female companion travel intentions: The mediating effect of female companion travel experience

When women travel together, the social identity of the members creates group harmony, but disagreements over travel arrangements create intragroup conflict. This study draws on the self-categorization theory to examine the impact of group conflict and group harmony on female companion travel experiences and future female companion travel intentions. The study used both qualitative and quantitative methods. Focus group meetings were performed to develop the scale to measure dictatorship as a source of creating group conflict during female companion travel. The results of the questionnaire survey indicated that group conflict and group harmony have an impact on female companion travel experiences, but only group conflict directly influences future female companion travel intentions, group harmony does not. Female companion travel experience mediates the effect of group conflict and group harmony on future female companion travel intentions. Recommendations for destinations and the tourism industry to improve female companion travel experiences are provided.

Multidimensional Dynamic Destination Recommender Search System Employing Clustering: A Machine Learning Approach

Objectives: Recommender Systems (RS) powered by algorithms of machine learning is a popular tool for planning and implementing custom-made travel proficiencies. The persistence of this study is to recommend destinations according to a selection of various dimensions by the user. Methods: This approach uses a hybrid filtering system for recommendation with a weighted K-means clustering algorithm. For this study dataset was taken from Kaggle. Data considers different cities of India with different dimensions like city, name, type, and significance. According to the city first find latitude and longitude for precise clustering. Future work will incorporate optimization techniques to improve cluster formation recommendation accuracy. Findings: Clustering (unsupervised learning) is a separation technique that involves assigning locations to corresponding subsets of related clusters. The weighted K-means clustering algorithm is used with the elbow method which is used for discovering the optimum number of clusters. In weighted K-means algorithm for clustering uses scaling factor wi ​which transforms the impression of individual features to the whole distance calculation. It signifies the meaning of the ith feature in the perspective of the grouping task. Offering a scaling factor permits additional tractability in modifying the outcome of specific features on the distance calculation. It enables customization of the distance metric constructed on the specific requirements and characteristics of the records and clustering task. In this study, user can select multiple dimensions of their choice and get recommendations according to their choice. The proposed weighted K-means algorithm shows a significant improvement in accuracy which considers the proportion of correct recommendations out of all recommendations. A comparison with traditional K-means was conducted, where the weighted algorithm achieved a 17% higher accuracy due to its ability to give importance to specific features. The future version of the proposed system will incorporate optimization techniques for enhanced performance. Novelty: The suggested solution in this paper demonstrates that the user can enter the city of their choice. The recommended method indicates the city and nearby predilections once the user has selected their parameters, such as consuming formations or name or type. The ratio of relevant destinations that have been successfully recommended is 18% more compared to the K-means clustering algorithm. Keywords: Recommender System, Clustering, Destination Recommender System (DRS), Machine Learning, Weighted K-means clustering Algorithm

Tourism Destination Recommendation based on bag of Visual Word Combined with SVM Classification

Tourism Destination Recommendation based on bag of Visual Word Combined with SVM Classification

BERTopic과 색채분석 기반 감성 추출을 이용한 관광지 추천 시스템 개발

This study aims to develop a personalized tourist destination recommendation system by analyzing the color and emotional factors of destination images. It evaluated and analyzed the emotional responses and color characteristics conveyed by images of natural resorts and urban tourist sites. A survey was conducted to collect users' emotional responses to these images, and a BERTopic model based on BERT was used to analyze text data from social media. The survey results provided a clear understanding of how users react to specific colors and emotional factors. For natural resorts, Green(G) was the most associated color, followed by Green-Yellow(GY) and Blue(B). In the case of urban tourist sites, Yellow-Red(YR) and Yellow(Y) were most commonly associated, followed by Blue-Green(BG) and Blue(B). Additionally, during the system's execution, the Bhattacharyya distance was used to measure color similarity between images, displaying images that match the user's emotions. Therefore, this study developed a recommendation system that considers the impact of destination images on users' emotions, proposing a new approach to better reflect users' emotional needs.

Application For Determining The Best Tourism In Jombang District Using The Weighted Product Method Based On Android

This research aims to develop an Android application that uses the Weighted Product (WP) method to determine the best tourist destinations in Jombang Regency. In today's digital era, the need for applications that can help tourists in choosing tourist destinations that suit their preferences and needs is increasing. The Weighted Product Method was chosen because of its ability to evaluate and give weight to various factors that affect tourist satisfaction, such as natural beauty, supporting facilities, accessibility, and safety. This research follows a systematic approach using the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) development model. The analysis stage includes identifying user needs, analyzing system requirements, and determining evaluation criteria. Furthermore, an application design that considers a friendly and functional user interface is created based on the results of the previous analysis. Application development is carried out by utilizing various latest technologies in Android software development. Application implementation involves trials using data on tourist destinations in Jombang Regency to test the effectiveness and accuracy in determining the best tourist destinations. Evaluation is done through feedback from users and analysis of comparison results between destination recommendations provided by the application and user preferences. It is expected that the results of this research will not only make a positive contribution to tourism development in Jombang Regency by improving the quality of service to tourists, but also become a foundation for further research in the development of similar applications using the Weighted Product Method.

Impact of Crisis and Resilience Communication on Destination Image and Tourists Behaviour

Resilience communication is a fundamental tool for destinations to improve their image and recover after a crisis. Social media are also important tools for tourism communication and for creating tourist experiences and destination image. Therefore, social media are key tools for crisis communication. Based on a series of hypotheses, this paper proposes a conceptual model of crisis communication management that responds to the need to establish a set of premises related to resilience communication in the tourism sector. The empirical analytical study consisted of the quantitative analysis of the results of a survey questionnaire applied to test the hypotheses based on the formulation of a structural equation model. The results demonstrate the importance of social media communication in Perceived Health Risk (PHR) and its effect on perceived impressions, destination image, and related behavioral responses (Destination Recommendation, DR, and Visit Intention, VI). The four hypotheses in the research model have been confirmed, as the effects are statistically significant in a sample of posts analyzed, and the selected items explain every one of the factors. Specifically, the analysis shows that PHR negatively affects Destination Image (DI) and, in a stronger way, Post Impression (PI). Secondly, PI and DI influence one another and covary to some extent. Moreover, the overall DI, which has been affected by PHR, in turn affects DR and VI, demonstrating that PHR in communication ultimately influences DR and VI negatively too. Finally, is also verified both DR and VI are related and covary as intrinsic behavioral aspects related to DI. Resilience communication is a basic tool for destinations to improve their image and recover after a crisis and this research contributes to the literature on the subject by analyzing the impact of this communication on tourists, and specifically on destination image.

A deep understanding of influencer marketing in the tourism industry: a structural analysis of unstructured text

ABSTRACT Using both a word frequency approach and a cutting-edge transfer learning technique for natural language processing with BERTopic, the present study analysed the entire texts from the top 40 travel influencers’ Instagram posts (n = 23,223). Among the 256 features that we initially extracted, we ranked the top 19 features using the machine learning algorithm XGBoost and estimated the effects of these features on consumer engagement using Negative Binomial regression. The results show that seasonal trips, travel destination recommendations, recommendations for fashion during the trip, and emphasising travel-related emotion generate a higher level of engagement. For message strategy, specifically focusing on linguistic features, it is recommended that influencers use analytic, authentic, want-related, and space-related words in the caption but should avoid using too many hashtags. Also, overall, influencers should avoid sending messages during the night, with messages that are too long or with too many emojis.

Learning Co-occurrence Patterns for Next Destination Recommendation

Learning Co-occurrence Patterns for Next Destination Recommendation

Development Tourism Destination Recommendation Systems using Collaborative and Content-Based Filtering Optimized with Neural Networks

Tourism, a vital sector in the global economy, benefits significantly from advancements in infrastructure, accessibility, and information availability. However, the vast volume of information can overwhelm travelers, underscoring the need for efficient recommendation systems. This research aims to develop an advanced tourist destination recommendation system by integrating Collaborative Filtering (CF) and Content-Based Filtering (CBF) models with Neural Networks. This approach seeks to enhance recommendation accuracy by closely aligning with user preferences and addressing the challenge of limited data. The study utilizes data from 523 tourist destinations across West Java, along with user preference assessments, encompassing stages of data collection, labeling, pre-processing, pre-training, neural network-based training, model evaluation, and the presentation of recommendation outcomes. The optimization of the recommendation models through neural networks has notably improved the precision of tourist destination suggestions, achieving Root Mean Square Error (RMSE) values below 0.37 for both CF and CBF approaches. This research significantly contributes to increasing the search efficiency and accuracy for tourist destination information, offering a strategic solution to the prevalent issue of information overload in the tourism industry.

Globe glider

Globe glider Tours and Travels aims to revolutionize the travel industry by offering a comprehensive platform that integrates seamless booking experiences, personalized trip planning, and cutting-edge travel recommendations. With an emphasis on leveraging advanced AI algorithms and machine learning techniques, Globe glider seeks to redefine the way travelers discover, plan, and embark on their journeys. Our platform will provide users with a holistic travel solution, from browsing destinations to booking accommodations, transportation, and activities, all tailored to their preferences and interests. Globe glider’s mission is the commitment to enhancing the traveler's experience through innovative technology and curated content. By harnessing vast datasets and employing sophisticated algorithms, our platform will deliver personalized recommendations for destinations, activities, and experiences based on each user's unique profile and past travel behavior. Additionally, Globeglider will offer real-time assistance and support throughout the travel journey, ensuring a seamless and stress-free experience from start to finish. In an ever-evolving travel landscape, Globe glider stands poised to empower travelers with the tools and resources needed to make informed decisions and create unforgettable experiences. With a focus on user-centric design, technological innovation, and unparalleled customer service, Globeglider aspires to become the go-to platform for travelers worldwide, facilitating seamless exploration, discovery, and adventure across the globe. Globeglider sets itself apart by prioritizing sustainability and responsible tourism practices. In an era where environmental conservation and ethical travel are paramount concerns, our platform will actively promote eco-friendly travel options, support local communities, and advocate for the preservation of cultural heritage sites. Through partnerships with eco-conscious suppliers and community-based tourism initiatives, we aspire to minimize the carbon footprint of travel while maximizing positive socio-economic impacts on host destinations. At the heart of GlobeGlider is a commitment to fostering a sense of exploration and connection among travelers. The application incorporates a user-centric approach, allowing individuals to customize their journeys based on preferences, interests, and travel goals. GlobeGlider not only streamlines the logistics of travel but also enhances the overall experience by offering curated suggestions, insider tips, and interactive maps. By combining convenience with a touch of adventure, GlobeGlider aims to empower users to embark on journeys that are not just destinations but transformative moments in their lives.

A Predictive Model Based on TripAdvisor Textual Reviews: Early Destination Recommendations for Travel Planning

Although many studies have considered the effects of online reviews on tourists’ decisions, none have directly investigated how to leverage open data analyses to create early choice sets and facilitate destination planning. This paper illustrates how salient characteristics can be mined from the shared experiences embedded in review data and incorporated into a predictive model to build a travel counseling approach. The model is designed by first defining a prediction-based mechanism from online reviews and then generating a multinomial classification problem on all candidate destinations of interest. The model is implemented by applying Natural Language Processing (NLP) and Deep Learning (DL) technologies to review textual features. The model is validated using 75,315 reviews from TripAdvisor along with destinations from 257 U.S. national parks. Empirical results indicate a best classification accuracy of 67%, outperforming two previous approaches. Findings shed light on how to exploit past tourists’ experiences to generate early destination recommendations to identify items for choice sets and reduce tourists’ travel-planning effort. Theoretical and managerial implications regarding social media analytics are provided based on online review meta-data in touristic management.

Building a unified communications centre to improve the distribution of EMS patients to a large multi-hospital health system

In 2023, hospital care-based models are faced with increasing patient volumes, limited physical space and limited resources. These constraints, felt in almost all acute-based care models, is leading to a crucial crossroads in acute care delivery. Balancing capacity and availability of hospital and system resources is almost impossible in this environment, as need greatly exceeds access to resources and patient care delivery can be significantly hindered. Historically, emergency medical services (EMS) brought patients to the nearest available emergency department (ED), and load balancing could only be accomplished after arrival in the ED. Intervening earlier in patient’s care by providing EMS with destination recommendations based on available resources optimises patient outcomes and decreases the burden on any individual hospital. This change can also greatly affect EMS processes to improve transport times and decrease wall time, the time that EMS crews spend at the hospital waiting to offload their patients into a hospital bed. Wall times can exceed several hours depending on location, time of day and patient resource needs. Reduction of this waiting time has the potential to profoundly improve throughput and patient-centred metrics like patient satisfaction, length of stay and admission rates, as well as reduce overall risk. This also allows health systems to maintain community resources by decreasing EMS crews’ idle time at the hospital. Through the creation of a unified communication centre (UCC), we sought to create a structure that appropriately stratified patients to the most appropriate system hospitals while still in the care of EMS. Our team’s goal was to optimise patient treatment, decrease wall time with EMS, and route patients to the most appropriate facility based on the patient’s medical complaints, hospital capacity and hospital capability in the community.

Perancangan Kampung Tematik River Tubing Mayangsari Menjadi Destinasi Wisata Kota Semarang

Thematic villages have a big role in reducing the poverty rate in Semarang City. The existence of tourism-oriented development will encourage thematic villages to have more impact on the people's economy. The River Tubing Mayangsari thematic village has the potential to be developed, therefore there is a need for planning and design recommendations for tourist destinations. The method used is a quantitative and qualitative descriptive analysis approach in data analysis and data collection techniques using field techniques and literature/document studies. The results obtained in this service are recommendations for socio-economic planning and infrastructure design for the Mayangsari River Tubing. This community service is expected to become a consideration and recommendation for the Regional Development Planning Agency for the City of Semarang in bringing the Mayangsari River Tubing Thematic Village to a Tourist Destination in the City of Semarang.

Socio-user Context Aware-Based Recommender System: Context Suggestions for A Better Tourism Recommendation

The existing tourism recommender system model is mostly predictive analytics for destination recommendations (item recommendation). Limited research has been conducted in the discussion of a recommender system model, particularly context suggestion. Thus, it is necessary to develop a recommender system model not only to predict tourism destinations but also to suggest contexts appropriate for tourist preferences (context suggestions). A deep learning method was used to create a model of the socio-user context aware-based recommender system for context suggestions. The attribute used as a label to suggest context was uHijos, uCuisine, uAmbience, and uTransport. The accuracy of the socio-user context aware-based recommender system in suggesting the context of uHijos, uAmbience, and uTransport was 100% with an error rate of 0%. It was found that only the level of recognition of the model in suggesting uCuisine was less accurate (below 30%) with a classification error for more than 70%. Performance evaluation of the socio-user model context-based recommender system was considered efficient, particularly for the evaluation of the level of accuracy, completeness (recall/sensitivity), precision, and a harmonic average of precision and recall (F-score), mainly for label/context of uHijos, uAmbience, and uTransport.

BERT-based tourism named entity recognition: making use of social media for travel recommendations.

Social media has become a massive encyclopedia of almost anything due to its content richness. People tell stories, write comments and feedback, and share knowledge through social media. The information available on social media enables 'clueless' travelers to get quick travel recommendations in the tourism sector. Through a simple query, such as typing 'places to visit in Bali', travelers can get many blog articles to help them decide which places of interest to visit. However, doing this reading task without a helper can be overwhelming. To overcome this problem, we developed Bidirectional Encoder Representations from Transformers (BERT)-based tourism named entity recognition system, which is used to highlight tourist destination places in the query result. BERT is a state-of-the-art machine learning framework for natural language processing that can give a decent performance in various settings and cases. Our developed tourism named entity recognition (NER) model specifies three different tourist destinations: heritage, natural, and purposefully built (man-made or artificial). The dataset is taken from various tourism-related community articles and posts. The model achieved an average F1-score of 0.80 and has been implemented into a traveling destination recommendation system. By using this system, travelers can get quick recommendations based on the popularity of places visited in the query frame. Based on the survey that we conducted to target respondents who have never visited and have no or limited knowledge about tourist attractions in some example cities, their average interest level from the recommendation results is higher than four on a scale of 1 to 5. Thus, it can be considered a good recommendation. Furthermore, the NER model performance is comparable to another related research.

Recommendation System for Mobile Applications Tour Guide and Travel Services using Demographic Filtering and Content-Based Filtering Methods based on Android

This research aims to build a tourism service recommendation system by providing recommendations for tour guides and tourism destinations using machine learning recommendation system methods. The recommendation system method in this research uses the demographic filtering method to recommend tour guides using a model with three stages, namely filtering, scoring and sorting the tour guide data category. At the scoring stage, it is carried out using a decision-making system method using the Simple Additive Weighting method to weight the tour guide value with the weighting value of each criterion in the tour guide data. This research also applies a content-based filtering method using a model with two stages, namely training and recommendation. The training stage uses tf-idf weighting and the recommendation stage uses cosine similarity to recommend tourism spots based on the similarity of metadata for each item. In using the recommendation system, the data will focus on collecting tour guide data and data on tourist attractions selected by the user. This data will be processed by a recommendation system using demographic filtering and content-based filtering methods. Through the Simple Additive Weighting method in demographic filtering and tf-idf weighting with consistent similarity in content-based filtering, this research found personalized recommendation results so that the level of accuracy of the recommendation results becomes more personal and accurate. The results of this research provide a recommendation system for tourist guides and tourist attractions which is implemented into an Android-based mobile application that can be used to meet tourists' needs.

Pengembangan Sistem Informasi Rekomendasi Wisata Kalimatan Timur Berbasis Web Menggunakan Metode Item-Based Collaborative Filtering

The development of a tourism object is an important sector in the development of an area to support the economic power of the people in the area, this also applies to the East Kalimantan province. In order to improve the tourist experience, the development of a tourist recommendation information system is a necessity. This research discusses the development of a web-based Tourism Recommendation Information System and Tourism Chatbot specifically for East Kalimantan Province.Item-Based Collaborative Filtering method is implemented in the development of this system. This method utilizes historical data of user preferences to recommend suitable tourist destinations. The data collected includes user preferences for various attractions, such as location, facilities, and types of activities. The development process starts from data collection and processing, building a web information system, to integration with a chatbot.Tourist Recommendation Information System provides recommendations for tourist destinations tailored to user preferences. Through a user-friendly web interface, users can explore interactive maps, find out the details of attractions, and see reviews from previous visitors. In addition, the Tourism Chatbot provides real-time information and recommendation services to users through an instant messaging platform. The trial results show that the Item-Based Collaborative Filtering method is able to provide recommendations that are relevant to user preferences. Users feel more assisted in planning their travel trips. The system also succeeded in increasing user interaction and engagement through the use of a responsive chatbot.In conclusion, the development of a web-based Tourism Recommendation Information System and Tourism Chatbot with the Item-Based Collaborative Filtering method can be an effective solution in improving the tourist experience in East Kalimantan Province. This system provides accurate recommendations and empowers users to plan a better and memorable tour.

The Impact of Turkish Literature on Tourism During the Republican Era and Alternative Destination Recommendations within the Scope of Literary Tourism

Edebiyat ve turizm, farklı özelliklere sahip iki disiplin olsa da birbirleri üzerindeki zamanla artan etkilerinin ve yakınlaşan ilişkilerinin sonucunda “edebiyat turizmi” kavramı ortaya çıkmıştır. Edebî eserlerin kişiler üzerinde bıraktığı etki okuyucuları birer turiste dönüştürebileceği gibi, turizmin de edebî eserlerin çeşitlenmesinde ve bilinirliğinin artmasında etkili olabileceği edebiyat turizmi aracılığıyla daha net anlaşılmıştır. Belirtilen ilişki çerçevesinde bu çalışmada Cumhuriyet Dönemi Türk Edebiyatı’nı turizme etkisi ve yeni edebiyat turizmi destinasyonları önerilmesi amaçlanmıştır. Çalışmada Cumhuriyet Dönemi Türk Edebiyatı’nın ön plana çıkan roman türüne ait eserleri başta olmak üzere, Cumhuriyet öncesi ve Modern Türk Edebiyatı eserleri de incelenerek bu eserlerdeki içeriklerin yeni bir edebiyat turizmi destinasyonuna elverişliliği değerlendirilmiştir. Yapılan incelemeler sonucu; Türk Edebiyatı’nın öncü yazarları Halide Edip Adıvar, Reşat Nuri Güntekin, Yakup Kadri Karaosmanoğlu, Orhan Kemal ve Halit Ziya Uşaklıgil’in romanlarından hareketle yeni edebiyat turizmi destinasyonları geliştirilebileceği anlaşılmıştır. Turizme kazandırılacak yeni turizm destinasyonlarının, Türkiye’nin 100 yıllık Cumhuriyet tarihinin zengin ve başarılı çıktıları olacağının, Cumhuriyet’in bundan sonraki yıllarında da Türk Edebiyatı’nın ve Türk turizminin hak ettiği değere ulaşmasında önemli bir görev üstleneceğinin altı çizilmiştir.

Unveiling trends in digital tourism research: A bibliometric analysis of co-citation and co-word analysis

Digital tourism has witnessed substantial evolution, shaping the trajectory of tourism research. Employing bibliometric analysis on 1,079 articles from the Web of Science database, this study identifies predominant themes and research trends in digital tourism. Key findings revealed three main co-citation clusters: ‘Smart tourism destinations and smart tourists', ‘Evolution and impact of E-tourism on travel behavior’, and ‘Personalized smart tourism experience’. Additionally, co-word analysis showcased prominent themes such as ‘AR-Integrated E-Tourism’, ‘Co-creation of smart tourism destinations in China post-COVID-19′, and ‘AI-driven personalized destination recommender systems in tourism’. The study highlights gaps in digital tourism research, advocating for socio-cultural preservation alongside tech advancements. Co-citation analysis suggests new travel theory directions, while AR's role in sustainable tourism is spotlighted. Practically, AI and Big Data emerge as pivotal in personalized experiences, with co-word analysis aiding industry foresight and emphasizing AI-driven, sustainable strategies. Limitations include reliance on a single database, suggesting future studies to integrate multiple sources and qualitative insights. The study's findings offer a roadmap for academics and practitioners, emphasizing potential avenues in digital tourism, especially in the context of sustainable and responsible practices.

AN ADVENTURE TOURISM STORY IN THE BRANDING PROCESS: HOT AIR BALLOON MANAGEMENT SUCCESSES IN CAPPADOCIA

Purpose- Adventure tourism has gained significant popularity in recent years, and its effective branding has become crucial for destinations to attract visitors. This research paper explores the role of hot air balloon management in the branding process of Cappadocia, a region renowned for its unique geological formations and rich cultural heritage. Cappadocia's hot air balloon rides have emerged as one of the most sought-after adventure tourism experiences in the world. Methodology- This study aims to investigate the factors contributing to the success of hot air balloon management in the branding process of Cappadocia.Using a qualitative research approach, data was collected through in-depth interviews with key stakeholders, including hot air balloon operators, local authorities, and tourists. The findings reveal several key factors that have led to the remarkable success of hot air balloon management in Cappadocia's branding efforts. First, the natural landscape of Cappadocia, with its stunning rock formations and fairy chimneys, provides a unique and picturesque backdrop for hot air balloon rides, creating an unforgettable experience for tourists. Additionally, the collaboration and cooperation among local stakeholders, including government bodies, tourism organizations, and hot air balloon operators, have played a crucial role in maintaining a high level of safety standards, operational efficiency, and visitor satisfaction. Findings- Furthermore, the marketing strategies employed by hot air balloon operators, such as social media campaigns, strategic partnerships, and targeted promotions, have effectively positioned Cappadocia as a must-visit adventure tourism destination globally. The seamless integration of hot air balloon rides into the broader tourism offerings of Cappadocia, including cave hotels, historical sites, and cultural experiences, has enhanced the region's overall branding and tourism appeal. Conclusion- This research paper highlights the importance of effective management practices in adventure tourism destinations and provides valuable insights for destination managers, policymakers, and tourism stakeholders seeking to develop and enhance adventure tourism branding. The findings contribute to the existing literature on adventure tourism management and offer practical recommendations for other destinations aiming to leverage their unique attractions in the branding process. Keywords: Adventure tourism, branding, hot air balloon management, Cappadocia, destination marketing, stakeholder collaboration JEL Codes: N7,N9,Z32