Attraction Recommendation Research Articles

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.

Impact of peer-to-peer online experience on cultural tourism marketing

Peer-to-peer (P2P) online experience offers low-cost immersive cultural experiences, but enterprises fear that it will negatively affect local tourism marketing. This study proposes a sequential mediation model to explore how P2P online experience stimulates travel intentions and attraction recommendations. We find that P2P online experience increases cultural presence, which in turn leads to higher situational empathy. This increased empathy then contributes to predicted tourism experience, which ultimately shapes behavioral intentions toward travel and attraction recommendations. Furthermore, the study finds that the impact of P2P online experience on behavioral intentions is stronger for consumers with lower cultural cognitive levels of tourist attractions. Our findings demonstrate the importance of P2P online experience on cultural presence and situational empathy in shaping travel behavior.

Development of Digital Marketing Applications for Jombang Tourism and Culture Based on Android (User)

ABSTRACT The development of global tourism is very rapid, including in Indonesia, especially in the city of Jombang. Jombang is known to have interesting cultural and natural tourism objects. Digital marketing describes the use of digital technology to support marketing functions. This research aims to study the use and development of digital marketing in the promotion of tourism and culture in Jombang. The digital marketing application developed is designed to help users find information about the natural beauty, tourist attractions, and cultural richness of Jombang. In addition, this application facilitates the search and discovery of included tourist sites, which is expected to have a positive impact on the development of tourism and cultural preservation in Jombang. This Android-based digital marketing application has several main features: Main Page (Home): Displays a summary or recommendation of tourist attractions in Jombang. Popular Tourism List: Presents the most visited tourist attractions. Maps: Helps users find routes to tourist sites. Tourist Description: Provides detailed information about a particular tourist destination. Popular Culture List: Displays cultural activities or events that are often held in Jombang. Tourist Type and Sub-district Filters: Allows users to select the type of tourism and sub-district of interest. Keywords : Development, Digital Marketing, Android, Tourism, Culture.

A multi-period intuitionistic fuzzy consensus reaching model for group decision making problem in social network

A new intuitionistic fuzzy consensus reaching model is developed with multi-period public opinions and expert evaluation values in social network environment. First, the public opinions are obtained by using the crawler software and sentiment analysis technology is used to transform public opinions into intuitionistic fuzzy decision matrix in each period. Attribute weights are calculated by using the time attenuation factor and changes in public opinion. Second, the social trust relationship is modeled and incomplete social trust relationships are completed by using Archimedean t-norm. The expert weights are calculated by using the dynamic trust degree and similarity degree. Third, a consensus framework is proposed for multiple-period decision making problem, which coordinates conflicts between experts through dual feedback paths. The collective opinion scores are calculated by using weights of periods and attribute weights obtained from the word frequency of public opinions. The tourism attraction recommendation method is used to illustrate the proposed method.

Consequences of Misclassification in Data Categorization for Tourism Attraction Recommendation DSS Using ARAS

This research focuses on optimizing tourism attraction management in Bali using DSS and the ARAS method, emphasizing the importance of accurate data categorization. Bali’s tourism industry, faced significant challenges during the COVID-19 pandemic, highlighting the need for effective management strategies. This study addresses these challenges by utilizing the ARAS method to analyze and rank tourist attractions. The research methodology follows the CRISP-DM model. The study demonstrates that improper use of conversion scales for quantitative data can lead to inaccurate rankings, as seen when comparing converted and non-converted data rankings. Alter01, Alter03, and Alter02 occupy the top three ranks in the non-converted data, while Alter09, Alter06, and Alter15 rank highest in the converted data. These findings highlight the need to use precise numerical values for criteria whenever possible and to reserve conversion scales for qualitative data, to ensure accurate and reliable recommendations. ARAS has a simple and easy-to-understand computational procedure. However, the results from ARAS heavily depend on the weights assigned to the criteria. Inaccurate determination of these weights can lead to outcomes that do not reflect actual preferences. The research concludes that implementing more refined data categorization techniques can enhance tourism management, promoting sustainable growth and more informed decision-making.

Cost-based distributionally robust tourist attraction recommendation with demand uncertainty

Tourist attraction recommendation (TAR) problem has gained attention due to its potential to enhance tourist services. Existing studies focus on meeting tourists’ individual needs, but overlook the tour operator’s interests as the TAR service provider. The TAR problem is more challenging due to the high variability of customer demand, which is difficult to predict accurately beforehand. This paper examines TAR in response to random changes in tourist demand, aiming to minimize transportation costs, cooperation expenses between tour operators and attractions, ticket booking fees, and promotion costs, where ambiguity set is defined by means, mean absolute deviations, and the support set. Firstly a distributionally robust model is proposed to identify suitable attractions for cooperation, along with determining the associated costs of ticket booking, promotion, and tourist transportation, while considering chance constraint on the service level. Subsequently, the model is reformulated into a tractable mixed integer linear programming model using duality theory. Numerical experiments illustrate that the proposed model outperforms both the stochastic programming model and the deterministic model in terms of risk level by out-of-sample test. In particularly, considering uncertainty and distributional ambiguity can make the model more accurate and credible.

Cultural tourism attraction recommendation model based on optimized weighted association rule algorithm

To address the challenge of users selecting rich tourism resources, this study proposes a model for cultural tourism attraction recommendation using an optimized weighted association rule algorithm. This model includes time and season weight for tourist attraction recommendations. This model proposes improvement methods to address some inherent issues in traditional tourism recommendation models. Firstly, it constructed a recommendation model for cultural tourism attractions, and then optimized the weighted association rule algorithm by incorporating dynamic time weights. It takes into account the user's intended time in the recommendation outcome. Moreover, it incorporated seasonal weights to optimize the weighted rule algorithm for factors such as user travel time and the attractions' peak season during the recommendation process. The experiment indicates that the F1 value of the improved algorithm model proposed in this study reaches 0.952, the accuracy reaches 0.985, the area under the curve reaches 0.955, the Recall value reaches 0.812, and the fitting degree reaches 0.971. The results suggest that the proposed cultural tourism attraction recommendation model, based on an optimized weighted association algorithm, performs well in recommending tourist destinations. This model can have a positive impact on the development of the tourism industry.

Integrating object detection and natural language processing models to build a personalized attraction recommendation agent in a smart product service system

Product Service System (PSS) is a new business model that integrates tangible products and intangible services to better meet customer needs and expectations. In recent years, scholars had some efforts to enhance the capability of PSS with the support of artificial intelligence (AI) which is known as Smart PSS(SPSS). So far, most previous studies adopted a single model which cannot handle multiple tasks simultaneously that results in unsatisfactory services to customers. In addition, customer preference cannot be fully addressed to achieve personalization. Therefore, this study proposes a method that integrates multiple models into an SPSS with three steps: (1) Collect data and construct an appropriate object detection model. (2) Develop smart PSS solutions. Then, (3) Optimize the system based on feedback through Natural Language Processing (NLP) to provide customers with personalized services. An attraction recommendation case study with experiment is designed to verify the proposed method. The results show that proposed SPSS can optimize the system in time according to the feedback of users, and provide better personalized services. This research is the first research that utilizes applied both text and image data to extract customer characteristics to better capture the voice of customers.

MMKG-PAR: Multi-Modal Knowledge Graphs-Based Personalized Attraction Recommendation

As the tourism industry rapidly develops, providing personalized attraction recommendations has become a hot research area. Knowledge graphs, with their rich semantic information and entity relationships, not only enhance the accuracy and personalization of recommendation systems but also energize the sustainable development of the tourism industry. Current research mainly focuses on single-modal knowledge modeling, limiting the in-depth understanding of complex entity characteristics and relationships. To address this challenge, this paper proposes a multi-modal knowledge graphs-based personalized attraction recommendation (MMKG-PAR) model. We utilized data from the “Travel Yunnan” app, along with users’ historical interaction data, to construct a collaborative multi-modal knowledge graph for Yunnan tourist attractions, which includes various forms such as images and text. Then, we employed advanced feature extraction methods to extract useful features from multi-modal data (images and text), and these were used as entity attributes to enhance the representation of entity nodes. To more effectively process graph-structured data and capture the complex relationships between nodes, our model incorporated graph neural networks and introduced an attention mechanism for mining and inferring higher-order information about entities. Additionally, MMKG-PAR introduced a dynamic time-weighted strategy for representing users, effectively capturing and precisely describing the dynamics of user behavior. Experimental results demonstrate that MMKG-PAR surpasses existing methods in personalized recommendations, providing significant support for the continuous development and innovation in the tourism industry.

The impact of Google Maps application on the digital economy

Google Maps is indispensable for helping tourists navigate Thailand, which is a popular tourist destination. It includes comprehensive maps, points of interest, and lodging, dining, and attraction recommendations. It contributes to Thailand’s tourism industry’s growth by enhancing the overall travel experience (Amornvivat et al., 2017; The Nation, 2023). This study explains the Google Maps application’s impact on Thailand’s digital economy. This research adopted interviews with six Google Maps consumers and six business owners. Content analysis and NVivo were used to analyse the data. The results show that the Google Map application simplifies locating businesses, making reservations, and placing orders for users. Additionally, Google Maps recommends nearby businesses that may interest the user based on their location and preferences. Through mobile commerce and local recommendations, it encourages users to explore new businesses and stimulates local spending. Google Maps has had a significant impact on the digital economy in Thailand. It helps businesses increase visibility, interact with customers, and attract foot traffic to their physical locations. It also facilitates mobile commerce, provides local recommendations, and aids the tourism industry. Google Maps contributes to digital domain economic growth, increased sales, and enhanced user experiences by connecting users with pertinent businesses and facilitating efficient navigation.

Attraction Recommendation and Itinerary Planning for Smart Rural Tourism Based on Regional Segmentation

Attraction Recommendation and Itinerary Planning for Smart Rural Tourism Based on Regional Segmentation

Tourist Attraction Recommendation Model Based on RFPAP-NNPAP Algorithm

Tourist Attraction Recommendation Model Based on RFPAP-NNPAP Algorithm

Rural tourism incorporating artificial intelligence technology to help modernize rural revitalization

Abstract In the process of modernization and development, intelligent technology empowers rural tourism, which in turn enables the effective implementation of the rural revitalization strategy. In this paper, a rural tourism recommendation model combined with artificial intelligence technology is constructed, which effectively improves the accuracy and personalization level of tourist attraction recommendation by mining and analyzing tourism data and extracting tourism characteristic factors. Secondly, the random forest classification algorithm is used to establish a random forest preference attraction prediction model, and several recommendation algorithms are combined to design a personalized rural tourism recommendation model to improve the accuracy of tourists’ tourism recommendations. Finally, the countryside around a city is being investigated to explore the modern development of rural tourism and help revitalize rural areas. The results show that the number of people received by rural tourism in City A and the number of tourists received by the province in 2022 is 972 million and 1,424 million more than that of 2014, and the total income from rural tourism in City A and the total income from tourism in the province in 2022 is 724.8 billion and 1,202.02 billion yuan more than that of 2014, and it can be seen that the high level of tourism income maintains a positive correlation with the number of tourists received. It shows that rural tourism improves the overall economic level of the countryside, accelerates the process of social progress in rural areas, and strongly contributes to the modernization and development of rural revitalization.

Innovative Research on Deep Learning Algorithm in Intelligent Attractions Recommendation Technology

Abstract In order to discover users’ interest preferences from the massive travel information and meet users’ needs for personalized tourist attraction recommendations. In this paper, we construct correspondingly according to the domain knowledge and complete the design and implementation of the mapping according to the corresponding process. The item representation enhancement module is designed to obtain a more comprehensive user preference by extracting information from the user’s historical preferences and then using graph convolution to learn the correlation between the user’s historical behavioral preferences and entities to enhance the KG semantic information of items. An item representation enhancement model based on RippleNet is proposed, from the item representation enhancement module to the interest module, to establish a connection between the user and the implicit information of the item so as to consider their mutual influence and represent the missing information more comprehensively, and finally, a personalized travel recommendation model based on knowledge graph and deep learning is constructed. From the results of the study, it is obtained that the model in this paper is 4.14% and 4.6% better than the best model SASRec in the comparison method in NDCG@10, Recall@10, MRR@10 indicators on both 1.93% higher. At the recommended list length K=20, the Recall rate Recall@20 has reached 80.78%. The superiority of the performance of the model constructed in this paper is demonstrated in the experiments, which brings technical innovation to the personalized attraction recommendation system.

Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations

The success of touristic businesses, attractions, and destinations heavily relies on travel agents’ recommendations, which significantly impact client satisfaction. However, the underlying recommendation process employed by travel agents remains poorly understood. This study presents a conceptual model of the recommendation process and empirically investigates the influence of tourism categories on agents’ destination recommendations. By employing collaborative filtering-based recommendation systems and comparing various algorithms, including matrix factorization and deep learning models, such as the bilateral variational autoencoder (BiVAE) and light graph convolutional neural network, this research provides insights into the performance of different techniques in the context of tourism. The models were evaluated using a tourism dataset and assessed through a range of metrics. The results indicate that the BiVAE algorithm outperformed others in terms of ranking and prediction metrics, underscoring the significance of considering multiple measurements and exploring diverse techniques. The findings have practical implications for tourism marketers seeking to influence travel agents and offer valuable insights for researchers investigating this domain. Additionally, the proposed model holds potential for applications in travel recommendation systems, including attraction recommendations.

Retracted: Tourist Attraction Recommendation Method and Data Management Based on Big Data Analysis

Retracted: Tourist Attraction Recommendation Method and Data Management Based on Big Data Analysis

Emotional expression by artificial intelligence chatbots to improve customer satisfaction: Underlying mechanism and boundary conditions

Artificial intelligence chatbots have invaded the tourism industry owing to their low cost and high efficiency. However, the influence of emotional expressions of chatbots on service outcomes has not received much attention from researchers. Drawing upon expectancy violations theory, we explored how emotional expressions of chatbots affect customer satisfaction using three experiments in the context of tourist attraction recommendations. Chatbots' expressions of concern for customers can improve customer satisfaction by reducing expectancy violations. In particular, customer's goal orientation, the human-likeness of chatbot's avatars, and the relationship type between customers and chatbots can moderate the negative relationship between emotional expression and expectancy violation. These findings advance research on the emotional expressions of chatbots and provide critical insights for deploying chatbots in customer service in the tourism industry.

The effect of intelligent tour guide system based on attraction positioning and recommendation to improve the experience of tourists visiting scenic spots

With the advancement of technology and economic development, human living standards have been substantially improved. The demand for tourism has also increased. Smart tourism uses information technology to integrate tourism resources. Tourists are provided with the right travel solutions for their needs. To further enhance the tourist experience, a smart tour guide system based on attraction positioning and recommendation is proposed. The RankSVM+time algorithm and K-means clustering algorithm are used to achieve attraction recommendation. The tourist flow path planning method of scenic spots is combined with D*algorithm to realize dynamic programming of tourist routes. A smart tour guide system based on tourist demand analysis is established to achieve data interaction and attraction positioning. The tour guide system is built based on analyzing the needs of tourists, realizing the functions of data interaction and attraction location. Tour guide services are provided in the form of voice or image. The experimental data shows that the F-value of RankSVM+time algorithm reaches 0.75. The recommendation accuracy is higher than that of RankSVM+Markov algorithm. The shortest running time is 81 s, which is faster than other methods. The intelligent tour guide system also dynamically adjusts the route visit scheme when dynamic changes occur in the attractions. The results show that the intelligent tour guide system based on attraction location and recommendation is highly accurate, fast and adaptable, which can enhance the tourist experience of visitors.

Attraction Recommendation Based on Tourism Context Modeling and Multi‐neural Collaborative Filtering Algorithm

The key to generating tailored suggestions in the scenic location recommendation scene is how to model the diverse tourism environments in order to correctly acquire visitor preferences and scenic spot tourism features. However, most existing recommendation algorithms focus on the spatiotemporal background modeling of historical tourism trajectories. Only tourists' preferences are understood, and the rich heterogeneous tourism information such as tourists' personal tourism constraints and scenic spots' tourism attributes are ignored. In this paper, we proposed a multiple neural collaborative filtering attraction recommendation architecture (MNCF‐AR). To begin, we learn the tourism feature representation of tourists by modeling diverse tourism contexts, and then we create the tourism trajectory background of tourists using a large number of actual tourism logs to achieve the entire feature representation of tourists. Second, to learn the feature vector of scenic spots in the context, the tourism heterogeneous network map is used to build the scenic spot attribute background, the self‐attention network to learn the scenic spot sequence learning, and a neural network to project each scenic spot into a unified potential feature space. Finally, the multi‐neural collaborative filtering method is utilized to forecast the difference in scores between visitors and scenic sites so that tailored scenic spots may be recommended. Extensive experiments on mainstream datasets, such as MaFengwo, New York, Tokyo and Xi'an, show that the proposed method can effectively and accurately recommend attractions for users. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

A Tourist Attractions Recommendation Model Based on Sequence Interest Extraction and Location Information Fusion

Travel, as one way to relax oneself, has become the first choice for people to enjoy their body and mind in modern society. However, while facing lots of information, how to help users make better decisions on their next travel goals through their historical interest spots is a direction that needs further research in big data recommendation systems. In this thesis, we proposed the deep convolution and multi-head self-attention position network model. First, it extracts the user’s historical interest point feature information by convolutional neural network method, and then performs horizontal and vertical filtering. Next, it interacts the obtained information with the candidate attraction information, and extracts the location information of the historical interest sequence by the multi-head self-attention mechanism. Finally, the model does the attention mechanism of the candidate attraction by fusing the feature information of the location information. The final model achieves a deep fusion of user sequence interest and location feature information. We conducted detailed comparison experiments with the very popular models in the industry on different public datasets, and the results showed that our deep convolution and multi-head self-attention position network model has good performance.