Tourism Recommendation Research Articles

Co-creation dynamics in tourism and hospitality: a horizon 2050 paper

Purpose The evolution of co-creation reflects a shift from a firm-centric to a user-centric and collaborative approach. The purpose of this study is to explore the benefits of co-creation, its potential drawbacks and implications for the tourism and hospitality industry to help frame the concept’s evolution and understand its importance for customizing and personalizing tourism experiences. Design/methodology/approach This conceptual study presents a holistic co-creation framework that encompasses various co-creation formats found over time. This study uses bibliometric analysis and thematic analysis powered by artificial intelligence tools as techniques. Findings Co-creation has transitioned from basic interactions between tourists and providers to sophisticated, technology-driven processes. This paper proposes a future-oriented framework that guides academic research and practical applications, outlining key innovation and sustainable growth areas in the tourism and hospitality sectors, demonstrating how co-creation fosters innovation, enhances consumer engagement and addresses sustainability challenges. Practical implications This study offers actionable recommendations for tourism firms to harness co-creation for hyper-personalized services, sustainability and community involvement. This study emphasizes the role of emerging technologies in shaping innovative and inclusive tourism experiences. Social implications This study promotes sustainable and ethical co-creation practices, highlighting the importance of inclusivity, cultural preservation and community empowerment in tourism development. Originality/value This reflection based on a bibliometric and artificial intelligence analysis helps to project the future developments of co-creation, raising new areas of research and paths to develop new theories and models to explain co-creation processes and outcomes and successfully integrate co-creation in tourism offers.

Modeling sustainable city trips: integrating $$\text {CO}_{2}\text {e}$$ emissions, popularity, and seasonality into tourism recommender systems

Modeling sustainable city trips: integrating $$\text {CO}_{2}\text {e}$$ emissions, popularity, and seasonality into tourism recommender systems

Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism

This paper intends to solve the limitations of the existing methods to deal with the comments of tourist attractions. With the technical support of Artificial Intelligence (AI), an online comment method of tourist attractions based on text mining model and attention mechanism is proposed. In the process of text mining, the attention mechanism is used to calculate the contribution of each topic to text representation on the topic layer of Latent Dirichlet Allocation (LDA). The Bidirectional Recurrent Neural Network (BiGRU) can effectively capture the temporal relationship and semantic dependence in the text through its powerful sequence modeling ability, thus achieving a more accurate classification of emotional tendencies. In order to verify the performance of the proposed ATT-LDA- Bigelow model, online comments about tourist attractions are collected from Ctrip.com, and users’ emotional tendencies towards different scenic spots are analyzed. The results show that this model has the best emotion classification effect in online comments of scenic spots, with the accuracy and F1 value reaching 93.85% and 93.68% respectively, which is superior to other emotion classification models. The proposed method not only improves the accuracy of sentiment analysis, but also provides strong support for the optimization of tourism recommendation system and provides more comprehensive, objective and accurate tourism information for scenic spot managers and tourism enterprises. This achievement is expected to bring new enlightenment and breakthrough to the research and practice in related fields.

Analisis Hubungan Promosi Dengan Minat Beli Wisatawan Terhadap Produk Olahan Komoditas Carica di Dataran Tinggi Dieng

Currently, social media is considered a convenient tool for facilitating product transactions. However, not all marketers possess effective strategies for promoting their products on social media. This study aims to analyze the relationship between promotion and tourists’ purchase intention toward processed carica products in the Dieng Highlands. The research was conducted from February to March 2024, involving 100 respondents and analyzed using simple linear regression. The findings reveal a significant relationship between promotion and tourists’ purchase intention toward processed carica products in the Dieng Highlands. Promotion contributed 30.5% to purchase intention, while the remaining 69.5% was influenced by other factors outside the scope of this study. Carica-based MSMEs in the Dieng Highlands optimize their promotion strategies through tourist recommendations and digital marketing. They actively use social media platforms such as Facebook, Instagram, and TikTok and market their products through e-commerce platforms like Shopee, Tokopedia, and TikTok Shop. These efforts aim to enhance accessibility, attract consumer interest, and expand market reach.

A multi-criteria trust-enhanced collaborative filtering algorithm for personalized tourism recommendations

The exponential growth of online information has LED to significant challenges in navigating data overload, particularly in the tourism industry. Travelers are overwhelmed with choices regarding destinations, accommodations, dining, and attractions, making it difficult to select options that best meet their needs. Recommender systems have emerged as a promising solution to this problem, aiding users in decision-making by providing personalized suggestions based on their preferences. Traditional collaborative filtering (CF) methods, however, face limitations, such as data sparsity and reliance on single rating scores, which do not fully capture the complexity of user preferences. This study proposes a hybrid multi-criteria trust-enhanced CF (HMCTeCF) algorithm to improve the accuracy and robustness of tourism recommendations. HMCTeCF improves the quality of recommendations by integrating multi-criteria user preferences with trust relationships among users and between items. Experimental results using real-world datasets, including Restaurants-TripAdvisor and Hotels-TripAdvisor, demonstrate that HMCTeCF outperforms benchmark CF-based recommendation methods. It achieves higher prediction accuracy and coverage rate, effectively addressing the data sparsity problem. This innovative algorithm facilitates a more personalized and enriching travel experience, particularly in scenarios with limited user data. The findings highlight the importance of considering multiple criteria and trust relationships in developing robust recommendation systems for the tourism industry.

Continued innovation beyond COVID-19 crisis: toward mitigating the challenges in the tourism and hospitality industry

PurposeThis paper aims to provide insights into service innovation (SI) during the COVID-19 crisis and its potential impact on tourism development in the medium-to-long term. The pandemic had a devastating effect on the industry, requiring immediate mitigation. It is yet to fully establish the impact of SI in the face of the COVID-19 volatility, uncertainty, complexity and ambiguity (VUCA). This study discusses the potential link between SI and COVID-19 crisis mitigation and offers recommendations for tourism recovery.Design/methodology/approachThis paper synthesizes empirical evidence on post-crisis tourism SI using a theory-based general literature review approach.FindingsCOVID-19 crisis spun various forms of SI, which emerged as a conventional solution to crisis prevention, encompassing the management of crisis-time competitiveness, revenue deficits and risk perception. However, resistance to innovative services is linked to situational conditions.Research limitations/implicationsCOVID-19 is an unprecedented crisis. Therefore, this study serves as a primer for further inquiry into SI. For instance, areas such as governance in tourism innovation and consumers' inclination toward innovation-driven services are underexplored.Practical implicationsSI acts as a situational facilitator, but its characteristics can impede or facilitate adoption. Moreover, the irrelevance of innovations in some environments is evidenced. Thus, practitioners must adopt a responsive learning approach in SI adoption. To mitigate the COVID-19 impacts, reconfiguration in SI, recovery marketing strategy, knowledge gap and governance will be critical interventions.Originality/valueThis paper is one of the first comprehensive discussions on the potential role of SI in mitigating the impact of COVID-19 on the THI.

Implementation Of MAUT Method for Making Website-based Tourism Recommendations in Yogyakarta based on Maps Location

Implementation Of MAUT Method for Making Website-based Tourism Recommendations in Yogyakarta based on Maps Location

Optimization of Tourism Destination Recommendations in Batang Regency Using Content-Based Filtering

In an era where tourism plays a pivotal role in economic development, the need for effective navigation through diverse attractions has never been more critical. This research presents a cutting-edge tourism recommendation system tailored for Batang Regency, leveraging Content-Based Filtering (CBF) to deliver personalized suggestions that enhance the tourist experience. By categorizing tourist attractions into Culinary, Culture, Accommodation, Nature, and Leisure, and employing the Haversine formula for precise geographical calculations, our system prioritizes recommendations based on user preferences and proximity. Recommendation testing yielded an impressive average F1 Score of 0.965, underscoring the system's accuracy and relevance, particularly in straightforward user scenarios. However, the research also identifies challenges in more complex cases, suggesting the need for future enhancements through hybrid models and the integration of user feedback. This innovative approach not only streamlines the decision-making process for tourists but also aims to boost local tourism, making it an invaluable tool for both visitors and the Batang Regency community. Join us in exploring how technology can transform the way we experience travel, ensuring that every journey is tailored to individual desires and needs.

Nipping trouble in the bud: A proactive tourism recommender system

Recommender systems have been used extensively in tourism activities to aid travel decision making. However, existing systems design routes using mainly deterministic information, ignoring the negative impacts of uncertain events, likely resulting in suboptimal or impractical route designs. This study addresses this issue by integrating forecasting mechanisms into system design, thereby enabling tourists to avoid potential risks and improving travel experience. For example, in crowding avoidance, the system's performance is demonstrated through a field experiment. The results suggest that our system outperforms benchmarks and can help tourists avoid attraction crowding. The system is also applicable for other predictable uncertain events.

Rural Tourist Attractions Recommendation Model Based on Multi-Feature Fusion Graph Neural Networks

With the rapid growth of the rural tourism industry, traditional tourism recommendation technologies can no longer meet the necessary requirements. To address the issue of rural tourist attraction recommendations, a rural tourist attraction recommendation model is constructed based on a multi-feature fusion graph neural network. First, construct a feature map based on the relationship between tourists’ preferences and tourist attractions, and incorporate the attention mechanism to enhance the model’s learning capabilities. Second, utilize a two-part graph model to extract positive and negative preference features of tourists, and a conversation graph model to extract tourists’ transfer preference features. Finally, various features are utilized to generate suggested content by computing scores for tourists’ travel preferences. To address the problem of recommending tourist groups, suitable features for random group matching are collected and the cosine function is employed to identify users with similar random group features. Finally, the multi-features are merged, and the tourists’ interest preferences are scored to arrive at content recommendations. In the experiment on individualized attraction recommendations, data from the Chengdu area were used to test the proposed model. The accuracy of the model’s recommendations was 0.822 for five recommendations which outperformed the other models. In the experiment for group-based attraction recommendations, this experiment tested the Chengdu dataset. The proposed model achieved the highest accuracy of 0.972 when the group size was 70, outperforming the other two models. Additionally, with regards to different numbers of recommendations, the proposed model’s accuracy was 0.5241, which was the best performance among the three models when the number of recommendations was set to five. The proposed recommendation model performs optimally in suggesting tourist attractions and meets the needs of rural tourism. The research content provides crucial technical references for tourist traveling and rural tourism development.

A Hybrid Intelligent Tourism Recommendation System Using Big Data and AI for Enhanced User-Centric Suggestions

A Hybrid Intelligent Tourism Recommendation System Using Big Data and AI for Enhanced User-Centric Suggestions

Reimagining sustainability: The power of AI and intellectual capital in shaping the future of tourism and hospitality organizations

This research investigates the effect of artificial intelligence (AI) on organizational sustainability (OS) and delves into how this relationship is mediated by various dimensions of intellectual capital (IC), namely human (HC), structural (SC), and relational capital (RC). Data were collected from employees of Saudi travel agencies, resulting in 1122 valid responses, which were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that AI positively impacts OS as well as the three IC dimensions. Additionally, HC, SC, and RC have a positive influence on OS. The study also reveals that these dimensions partially mediate the relationship between AI and OS. This study advances the literature by clarifying how AI-enabled capabilities are translated into sustainable outcomes within the tourism and hospitality sectors. The findings offer valuable insights for industry professionals aiming to harness AI technologies for sustainable transformation. The study also offers strategic recommendations for tourism and hospitality organizations to invest in enhancing their IC, thereby optimizing the sustainability advantages of AI integration.

Tourism Recommendation Algorithm Based on the Mobile Intelligent Connected Vehicle Service Platform

As to the problems in current tourism recommendation, this paper proposes a tourism recommendation algorithm based on the mobile ICV service platform. Firstly, the ICV service system for the Point of Interest (POI) searching and route recommendation is designed. Secondly, the recommendation service model is set up from two aspects, namely the tourism POI clustering algorithm and the tourism POI searching and route recommendation algorithm. In the aspect of symmetrical-based matching features, the clustered POIs are matched with the tourists’ interests, and the POIs in the neighborhood of the ICV dynamic locations are searched. Then, a POI recommendation algorithm based on the tourists’ interests is constructed, and the POIs that best match the symmetrical interests of the tourists within the dynamic buffer zones of ICV are confirmed. Based on the recommended POIs, the ICV guidance route algorithm is constructed. The experiment verifies the advantages of the proposed algorithm on the aspect of the POI matching tourists’ interests, algorithm stability, traveling time cost, traveling distance cost and computational complexity. As to the iterative sum and the iterative sum average of the POI matching function values, the proposed algorithm has a performance improvement of at least 20.2% and a stability improvement of at least 20.5% compared to the randomly selected POIs in matching tourists’ interests. As to the cost of the guidance routes, the proposed algorithm reduces the average cost by 19.6% compared to the other suboptimal routes. Compared with the control group algorithms, the proposed algorithm is superior in terms of route cost, with an average cost reduction of 13.8% for the output routes compared to the control group. Also, the proposed algorithm is superior in terms of route cost compared to the control group recommendation algorithms, with an average cost reduction of 11.2%.

Sistem Pendukung Keputusan Pemilihan Objek Wisata Kabupaten Berau Menggunakan Metode Additive Ration Assessment (ARAS)

Indonesia is a country that is famous for its natural wealth and tourist destinations worldwide, and it is spread out in various regions. One of the regions in East Kalimantan that is popular and often visited by tourists every year because it is famous for its stunning underwater tourist destinations is the Berau Regency. According to the East Kalimantan Tourism Office, this district has the highest number of tourist attractions in East Kalimantan, which is 224. Many tourist attractions trigger confusion among tourists when deciding which alternative option to visit. Therefore, this research aims to build a decision support system to assist tourists in getting alternative tourism recommendations that are appropriate and to their needs. The method used in this research is the Additive Ratio Assessment (ARAS) method. There are 30 alternative attractions and 5 criteria: facilities, safety, cleanliness, cost, and distance. The results show that the ARAS method can recommend the best alternative to tourist attractions. The accuracy testing results using the confusion matrix method get a percentage value of 80%.

SABTR: semantic analysis-based tourism recommendation

Online tourism spot recommendations, as a key component of tourism services, aim to present travel options that align with users’ personal preferences. However, current recommendation systems often underperform due to the sparsity of tourism data and the wide variance in user preferences. To address this challenge, we propose a Semantic Analysis-Based Tourism Recommendation framework, abbreviated as SABTR (Semantic Analysis-Based Tourism Recommendation). The framework comprises two stages: Firstly, Latent Dirichlet Allocation (LDA) models are utilized to deeply mine data between users and attractions, constructing two core matrices: the user similarity matrix and the attraction similarity matrix. Secondly, based on the user similarity matrix, similarity calculation methods are applied to predict ratings for tourism spots that users have not yet evaluated. Simultaneously, within the attraction similarity matrix, probability distributions for each attraction across various thematic interests are calculated. When the system identifies a user’s interest in specific types of attractions, SABTR can select a series of related attractions from associated interest tags. Then, these candidate attractions are ranked according to both known and predicted user ratings, ultimately forming personalized attraction packages recommended to users. Extensive experiments have demonstrated that compared to existing tourism recommendation solutions, our method significantly improves the quality of attraction recommendations and enhances user satisfaction.

Optimizing the Learning Rate Hyperparameter for Hybrid BiLSTM-FFNN Model in a Tourism Recommendation System

Indonesia, with its abundant natural resources, is rich in captivating tourist attractions. Tourism, a vital economic sector, can be significantly influenced by digitalization through social media. However, the overwhelming amount of information available can confuse tourists when selecting suitable destinations. This research aims to develop a tourism recommendation system employing content-based filtering (CBF) and hybrid Bidirectional Long Short-Term Memory Feed-Forward Neural Network (BiLSTM-FFNN) model to assist tourists in making informed choices. The dataset comprises 9,504 rating matrices obtained from tweet data and reputable web sources. In various experiments, the hybrid BiLSTM-FFNN model demonstrated superior performance, achieving an accuracy of 93.36% following optimization with the Stochastic Gradient Descent (SGD) algorithm at a learning rate of about 0.193. The accuracy, after applying Synthetic Minority Over-sampling Technique (SMOTE) and fine-tuning the learning rate hyperparameter, showed a 14.3% improvement over the baseline model. This research contributes by developing a recommendation system method that integrates CBF and hybrid deep learning with high accuracy and provides a detailed analysis of optimization techniques and hyperparameter tuning.

Genetic Algorithm Optimization of Hybrid LSTM-AutoEncoder in Tourism Recommendation System

The tourism industry has rapid growth and has become one of the world's leading economic industries in recent years due to advances in information technology, such as the internet and social media. However, the overwhelming amount of information often makes it difficult for travelers to decide on their preferred travel destination. To address these issues, this research proposes a tourism recommendation system that combines Content-Based Filtering and Hybrid LSTM-AE, which is optimized using Genetic Algorithm (GA). There is no research that has developed a recommendation system using a combination of these methods and optimized using GA. So that this research can contribute to providing personalized recommendations and higher accuracy. The dataset consists of 9,504 ratings collected from the Ministry of Tourism and Creative Economy, Twitter, and web sources. The system was able to achieve a rating prediction accuracy of 96.82% by applying SMOTE to handle data imbalance and implementing a GA approach to the Hybrid LSTM-AE model. Accuracy has increased by 18.7% from the baseline model without using SMOTE and optimization. These results underscore that a strong integration between natural language processing and genetically optimized deep learning provides more accurate recommendations.

Trends In Tourism Recommendation Systems: A Review

Tourism Recommendation Systems (TRS) are increasingly important in the tourism industry to provide personalized recommendations based on diverse tourist preferences. Technology and big data have transformed TRS from traditional travel agencies to modern digital platforms, enabling the processing of vast amounts of user-generated data for precise recommendations. The study aims to identify strengths and weaknesses within existing TRS frameworks and techniques, propose recommendations to mitigate these weaknesses, and provide insights for practitioners and researchers. Key findings are the effectiveness of personalized and context-aware recommendations, the importance of multimodal data integration, the need for ethical and fair recommendation practices. Future directions in TRS research should focus on exploring and developing explainable AI and transparency, personalization at scale, enhancing multimodal recommendation capabilities, and ensuring ethical and fair recommendation. This review contributes to a deeper understanding of contemporary TRS methodologies and provides actionable insights for enhancing TRS performance. By addressing current trends and proposing recommendations for future research, this paper aims to advance the field of TRS and improve travel experiences for tourists.

Preliminary Data on the Species Composition and Distribution of Land Snail (Mollusca: Gastropoda) in Son Doong Cave, Phongnha - Kebang National Park

Son Doong Cave it is the largest cave on the planet and also the most majestic cave in Vietnam. Son Doong Cave is divided into habitats: Dark cave (covered by the cave ceiling) and light cave (not covered by the cave ceiling), including doline 1 and doline 2.Surveying and collecting land snail samples in Son Doong cave was carried out in January 2023. For the first time, the list of terrestrial snail species in Son Doong Cave was published. This includes 30 species (11 phenotyps are just only identified to genus) belonging to 24 genera, 11 families, 3 orders, 3 subclasses; of which the presence of 5 species (Pincerna mouhoti, Leptopoma perlucidum, Scabrina patera, Chloritis khammouanensis, Amphidromus haematostoma) are newly recorded and added to the list of terrestrial gastropod fauna of Vietnam. In Son Doong cave, land snails are only distributed in the light part of the cave (Doline 1 and Doline 2), not in the dark part of the cave, which accounts for most of the cave area. Doline 2 has a higher species diversity (27 species belonging to 22 genera, 8 families, 2 orders) than Doline 1 (13 species belonging to 11 genera, 6 families, 3 orders). The diversity of land snail species in Son Doong cave is very unique and highly distinctive, but the species populations here is very small, requiring recommendations for tourists and the management board to protect vegetation to maintain and develop populations of this species.

Web-Based Decision Support System for Budget Prediction Using the SAW Method for Tourism Recommendations

Tourism plays a crucial role in regional and national economies, with local tourism in Indonesia, particularly in Jombang Regency, East Java, offering substantial development potential. However, tourists often face difficulties in planning trips that align with their budgets due to inadequate integration of information on costs, trip duration, and participant numbers. This study develops a web-based Decision Support System (DSS) utilizing the Simple Additive Weighting (SAW) method to enhance trip planning for tourism in Jombang. The DSS integrates data on budget, trip duration, and the number of participants to provide optimal recommendations based on user preferences and constraints. The SAW method facilitates a systematic and objective evaluation process for recommending tourist destinations. The research follows the ADDIE development model, encompassing Analysis, Design, Development, Implementation, and Evaluation stages. Key objectives include creating a system that consolidates cost, duration, and participant data, applying SAW for accurate recommendations, and ensuring an objective evaluation process. The system aims to improve trip planning efficiency, provide accurate recommendations, and enhance user satisfaction. The scope of this study is limited to applying the SAW method for budget prediction in tourism recommendations for Jombang. The developed system is a prototype that may require further refinement for comprehensive implementation.