Tourist Attraction Recommendation Research Articles

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.

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.

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

K-Nearest Neighbors Method for Recommendation System in Bangkalan’s Tourism

The more tourist objects are in an area, the more challenging it is for local governments to increase the selling value of these attractions. The government always strives to develop tourist attraction areas by prioritizing the beauty of tourist attractions. However, visitors often have difficulty in determining tourist objects that match their criteria because of the many choices. The research developed a tourist attraction recommendation system for visitors by applying machine learning techniques. The machine learning technique used was the K-Nearest Neighbor (KNN) method. Several trials were conducted with a dataset of 315 records, consisting of 11 attributes and 21 tourist attractions. Based on the dataset, the preprocessing stage was previously carried out to improve the data format by selecting data where the data were separated based on existing criteria, then calculating the closest distance and determining the value of k in the KNN method. The results are divided into five folds for each classification method. The highest system accuracy obtained at KNN is 78% at k=1. It shows that the KNN method can provide recommendations for three tourist attraction classes in Bangkalan. Applying the KNN method in the recommendation system determines several alternative tourist objects that tourists can visit according to their criteria in natural, cultural, and religious tourist objects.

Intelligent recommendation model of tourist places based on collaborative filtering and user preferences

ABSTRACT Providing personalized recommendation service for users and improving the accuracy of recommendation and user satisfaction are the main research tasks of current travel recommendation systems. The intelligent recommendation model of tourist places requires the algorithm to be able to accurately recommend tourist attractions according to the user’s interests. Big data and artificial intelligence technologies have driven the development of intelligent recommendation systems. However, realistic data are often sparse, and the lack of common rating items among users makes some traditional similarity measures impossible to calculate. In addition, traditional collaborative filtering algorithms ignore the issue of user preferences, which can cause a decrease in recommendation accuracy. To address these issues, this paper analyzes the factors affecting users’ interest preferences in terms of their global and local rating information. The interest preferences of users are calculated by computing the probability distribution of user rating information globally and using Hamming approach degree. A similarity algorithm about user preferences is derived using Jeffries-Matusita distance. The similarity algorithm is effectively combined with the traditional similarity algorithm to propose a model of collaborative filtering recommendation algorithm for tourist attractions based on user preferences under sparse data. The paper aims to improve the accuracy of tourist attraction recommendation systems by incorporating user preferences and addressing the issue of sparse data and the lack of common rating items among users that traditional similarity measures cannot calculate. The experimental results show that the improved algorithm model outperforms the traditional collaborative filtering algorithm and other algorithms. And it also has high accuracy rate on more sparse tourism data set.

Multi-Level Visual Similarity Based Personalized Tourist Attraction Recommendation Using Geo-Tagged Photos

Geo-tagged photo-based tourist attraction recommendation can discover users’ travel preferences from their taken photos, so as to recommend suitable tourist attractions to them. However, existing visual content-based methods cannot fully exploit the user and tourist attraction information of photos to extract visual features, and do not differentiate the significance of different photos. In this article, we propose multi-level visual similarity-based personalized tourist attraction recommendation using geo-tagged photos (MEAL). MEAL utilizes the visual contents of photos and interaction behavior data to obtain the final embeddings of users and tourist attractions, which are then used to predict the visit probabilities. Specifically, by crossing the user and tourist attraction information of photos, we define four visual similarity levels and introduce a corresponding quintuplet loss to embed the visual contents of photos. In addition, to capture the significance of different photos, we exploit the self-attention mechanism to obtain the visual representations of users and tourist attractions. We conducted experiments on two datasets crawled from Flickr, and the experimental results proved the advantage of this method.

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

The proliferation of social networking data has opened up numerous avenues for providing additional perspectives to decision-makers. While big data analytics has the potential to aid in rational decision, so far there is little evidence to support this claim. More importantly, in the tourism industry specifically, a standardized approach to assessing social video big data for strategic planning has not yet been created. This project will use a research-based design science method to develop and estimate a “big data analytics” strategy for enhancing strategic decision-making in the control of tourist destinations. Using geotagged pictures provided by visitors to the picture social-media site Flickr as a matter of reality, with Melbourne, Australia as a case study, this method’s applicability is demonstrated in helping destination management organizations analyze and predict tourist behavioral patterns at specific destinations. Extra source, recipient, and stakeholder groups were used to verify relevance. The produced artifact exemplifies a technique for assessing massive amounts of unstructured data to aid strategic decision-making in an actual problem area. The scope of the suggested method is examined, and it is possible that it could be applied to other types of extremely large data sets.

Optimization of Digital Recommendation Service System for Tourist Attractions Based on Personalized Recommendation Algorithm

With the deepening of tourists’ demand for tourism services, the personalization of online tourists has gradually become an application of personalized recommendation technology. According to the application requirements of personalized scenic spot recommendation, this paper uses social networks and Bayesian networks to fully mine the matching degree between users and scenic spots for personalized recommendation. Add social network factors to the recommendation of tourist attractions, and fully tap the social network relationship between users. First, the users are clustered by the coupling bidirectional clustering algorithm. Then, DBSCAN (density-based noise application spatial clustering) algorithm is used to cluster scenic spots. Finally, two stable user sets and scenic spot sets are applied to the personalized recommendation algorithm to predict the user’s next upcoming scenic spot. The algorithm is compared with some traditional algorithms in the dataset. The algorithm deals with the similarity of user attributes and user behavior and uses content-based algorithm to deal with the relationship between scenic spots; com datasets have better performance.

EXPERT SYSTEM TO GUIDE USERS OF THE TOURIST CORRIDOR OF THE PROVINCES OF JAEN, SAN IGNACIO AND UTCUBAMBA IN CAJAMARCA, PERU

Purpose: Develop a rule-based expert system to guide the users of the tourist corridor of the provinces of Jaén, San Ignacio and Utcubamba (Peru).
 Theoretical framework: Hussein and Aqel (2015), developed a rule-based expert system in Jordan to choose the best tour package based on time, budget, and preferences. In Peru, Ramos and Valdivia (2017), proposed an expert system to promote tourism in the Lambayeque region.
 Methodology: To develop the system, the methodology of Nicolás Kemper was used. Tourism experts from the provinces participated in the development of the knowledge base. The evaluation was carried out with an expert different from those who prepared the aforementioned base.
 Findings: In the evaluation, the expert system and the human expert agreed on the recommendation of tourist attractions by 80%. Concluding that this system helps tourists in making decisions about which places to visit in the tourist corridor.
 Contributions: The system helps improve the dissemination of local tourist information. To develop the knowledge base, tourism resources were systematized. New variables can be incorporated into the knowledge base in order to obtain more personalized tourist recommendations.
 Originality/value: This research is innovative because there is no expert system to guide tourists who want to travel to these places; it has social relevance as it helps to boost the local economy.

Recommendation Model of Tourist Attractions Based on Deep Learning

In order to solve the problem of tourism information overload caused by the rapid development of tourism and the Internet era, the author proposes a tourist attraction recommendation model based on deep learning. Convolutional Neural Network (CNN) is used to extract the sentiment of text comments, the Pearson similarity formula is used to calculate similar user groups, and the mean absolute error (MAE) is used to evaluate the resulting error. Compare with traditional collaborative filtering methods. Experimental results show that: the MAE value is smaller than the MAE value of the collaborative filtering method, indicating that considering tourists’ behavioral information, contextual information, and emotional factors in comments can effectively improve the accuracy of recommendation, as the data volume of the test set increased from 250 to 2000; although there was an increase in the MAE value, the overall trend showed a downward trend, indicating that the quality of the model can be more fully verified when the data volume is large. The model proposed by the author can effectively reduce the prediction error and improve the efficiency of tourist attractions recommendation.

Implementation of Personalized Information Recommendation Platform System Based on Deep Learning Tourism

In order to provide tourists with better tourism services, a system method of personal information recommendation platform based on deep learning tourism is proposed. The system includes noise reduction autoencoder, feature extraction module, data preprocessing module, recommendation calculation module, expert evaluation module, recommendation result output module, customer feedback module, and storage module. The personal information recommendation platform system based on deep learning tourism of the present invention enables tourists to obtain tourism information conveniently and quickly through scientific information organization and presentation form and helps tourists to better arrange tourism plans and form tourism decisions. By effectively aggregating multiple neighborhoods of nodes, embedding high-order collaboration information into the node embedding vector, obtaining the potential preferences of users, solving the problems of user data sparse and cold start, and finally through experimental analysis, a research method is proposed. It is used to build the model of tourist attraction recommendation system. Experimental results show that the proposed method for cold-start user recommendation has the best performance in terms of accuracy, recall, and normalized loss cumulative gain, and it is 17.9% higher than BPR in recall rate Recall@5 and 11.8% higher in accuracy rate. It is proved that the system has a significant impact on the diversity and novelty of tourist attraction recommendation.

Tourist Attraction Recommendation Method Based on Megadata and Artificial Intelligence Algorithm

As China’s economy continues to grow of informational technology and mobile Internet industry, the online tourism industry has received more and more extensive attention and use. However, as an emerging industry, users often need to spend a lot of time to choose travel services that match their needs because of the complex amount of relevant information. Under such circumstances, this paper studied the recommendation method in travel platform. First, the big data is used to extract user data. Secondly, the current online travel business recommendation for users has the problem of low accuracy. The reason is that the services provided are still in traditional recommendation algorithm. In this paper, the Bayesian network is used to evaluate the user’s attribute preference and generate a data model, using effective methods in artificial intelligence algorithms to improve collaborative filtering algorithms and finally generate hybrid recommendation algorithms. Compared with the traditional recommendation method, the experimental results showed that the research can improve the recommendation accuracy of tourist attractions by 6.55%, increase the user’s satisfaction for the platform, and enhance the visit rate and retention rate of the tourist attraction recommendation platform.

Personalized Recommendation Algorithm of Smart Tourism Based on Cross-Media Big Data and Neural Network.

Accurate recommendation of tourist attractions is conducive to improving users' travel efficiency and tourism experience. However, the choice of tourism feature factors and the difference of recommendation algorithm will affect the accuracy of scenic spot recommendation. Aiming at the problems of sparse data, insufficient tourism factors, and low recommendation accuracy in the existing tourism recommendation research, this paper puts forward a scenic spot recommendation method based on microblog data and machine learning by using the characteristics of personalized expression and strong current situation of microblog data and the intelligent prediction function of machine learning, so as to realize accurate and personalized scenic spot recommendation. This paper extracts rich tourism characteristic factors. Typical tourism recommendation algorithms choose tourism characteristic factors from scenic spots, tourists, and other aspects, without considering the travel time, tourism season, and other contextual information of tourists' destination, which can help understand users' tourism preferences from different angles. Aiming at the problem of sparse data and cold start of collaborative filtering recommendation algorithm, this paper introduces deep learning algorithm and combines the proposed multifeature tourism factors to build dynamic scenic spot prediction models (random forest preferred attraction prediction (RFPAP) and neural networks preferred attraction prediction (NNPAP)). The experimental results show that RFPAP and NNPAP methods can overcome the problem of data sparsity and achieve 89.61% and 89.51% accuracy, respectively. RFPAP method is better than NNPAP method and has stronger generalization ability.

Optimization of Rural Smart Tourism Service Model with Internet of Things

Rural smart tourism is a new tourism form supported by information technology, especially the Internet of Things technology. Smart tourism not only makes it easier for tourists to get diverse tourism information, but it also helps attractive sites improve their service capabilities. The optimization of the rural smart tourist service model, which is explored in this work, includes personalized recommendations of scenic sites. The main research contents of the paper are as follows: (1) A new algorithm for recommending tourism destinations with domain adaptability has been suggested. In the field of personalized recommendation of tourist attractions, most of the target domain data are unlabeled, and the model cannot be trained. When it comes to training, however, there exist source domain data sets that are completely labeled and can be used as a supplement to the target domain training data sets. (2) A personalized recommendation algorithm for tourist attractions with deep migration has been studied. Owing to the distribution difference between target data and source data, an adaptive layer is added to the convolutional neural network. The domain loss is then minimized to achieve deep feature transfer, and the model can then be trained to find scenic areas of interest to the user. Finally, the recommendation for tourist destinations with deep migration is done by examining the relationship between the target user and other users.

Deep learning and Internet of Things for tourist attraction recommendations in smart cities

We propose a tourist attraction IoT-enabled deep learning-based recommendation system to enhance tourist experience in a smart city. Travelers will enter details about their travels (traveling alone or with a companion, type of companion such as partner or family with kids, traveling for business or leisure, etc.) as well as user side information (age of the traveler/s, hobbies, etc.) into the smart city app/website. Our proposed deep learning-based recommendation system will process this personal set of input features to recommend the tourist activities/attractions that best fit his/her profile. Furthermore, when the tourists are in the smart city, content-based information (already visited attractions) and context-related information (location, weather, time of day, etc.) are obtained in real time using IoT devices; this information will allow our proposed deep learning-based tourist attraction recommendation system to suggest additional activities and/or attractions in real time. Our proposed multi-label deep learning classifier outperforms other models (decision tree, extra tree, k-nearest neighbor and random forest) and can successfully recommend tourist attractions for the first case [(a) searching for and planning activities before traveling] with the loss, accuracy, precision, recall and F1-score of 0.5%, 99.7%, 99.9%, 99.9% and 99.8%, respectively. It can also successfully recommend tourist attractions for the second case [(b) looking for activities within the smart city] with the loss, accuracy, precision, recall and F1-score of 3.7%, 99.5%, 99.8%, 99.7% and 99.8%, respectively.

A Smart Tourism Recommendation Algorithm Based on Cellular Geospatial Clustering and Multivariate Weighted Collaborative Filtering

Tourist attraction and tour route recommendation are the key research highlights in the field of smart tourism. Currently, the existing recommendation algorithms encounter certain problems when making decisions regarding tourist attractions and tour routes. This paper presents a smart tourism recommendation algorithm based on a cellular geospatial clustering and weighted collaborative filtering. The problems are analyzed and concluded, and then the research ideas and methods to solve the problems are introduced. Aimed at solving the problems, the tourist attraction recommendation model is set up based on a cellular geographic space generating model and a weighted collaborative filtering model. According to the matching degree between the tourists’ interest needs and tourist attraction feature attributes, a precise tourist attraction recommendation is obtained. In combination with the geospatial attributes of the tourist destination, the spatial adjacency clustering model based on the cellular space generating algorithm is set up, and then the weighted model is introduced for the collaborative filtering recommendation algorithm, which ensures that the recommendation result precisely matches the tourists’ needs. Providing precise results, the optimal tour route recommendation model based on the precise tourist attraction approach vector algorithm is set up. The approach vector algorithm is used to search the optimal route between two POIs under the condition of multivariate traffic modes to provide the tourists with the best motive benefits. To verify the feasibility and advantages of the algorithm, this paper designs a sample experiment and analyzes the resulting data to obtain the relevant conclusion.

Location Identification and Personalized Recommendation of Tourist Attractions Based on Image Processing

Currently, tourists tend to plan travel routes and itineraries by searching for relevant information on tourist attractions via the Internet and intelligent terminals. However, it is difficult to achieve good retrieval effect on tourist attraction images with text labels. Based on deep learning, the visual location identification faces such defects as frequent mismatching, high probability of weak matching, and long execution time. To solve these defects, this paper puts forward a novel method for location identification and personalized recommendation of tourist attractions based on image processing. Specifically, the authors detailed the ideas and steps of the location identification algorithm for tourist attractions. The algorithm, grounded on hash retrieval, encompasses two stages: an offline stage, and an online stage. Besides, a personalized recommendation model for tourist attractions based on geographical location and time period. Finally, the proposed algorithm and model were proved accurate and effective through experiments.

A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos

The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content and contextual information that may influence the recommendation accuracy, especially tourist attractions’ visual content due to redundant and noisy geotagged photos; therefore, we propose a tourist attraction recommendation model for Flickr-geotagged photos which fuses spatial, temporal, and visual embeddings (STVE). After spatial clustering and extracting visual embeddings of tourist attractions’ representative images, the spatial and temporal embeddings are modeled with the Word2Vec negative sampling strategy, and the visual embeddings are fused with Matrix Factorization and Bayesian Personalized Ranking. The combination of these two parts comprises our proposed STVE model. The experimental results demonstrate that our STVE model outperforms other baseline models. We also analyzed the parameter sensitivity and component performance to prove the performance superiority of our model.

Knowledge Discovery And Virtual Tour To Support Tourism Promotion

Planning a tourism trip is an important part for tourists so that their tour is satisfying. Service bureaus that have a function to help provide information and prepare tourist travel plans for tourists often provide random destination choices because they do not know the pattern of selecting tourist destinations. This will be detrimental to tourists when service bureaus make wrong tourism travel plans. Tourists also often find it difficult to determine which tourist destination to go to because they do not know the environmental conditions in tourist destinations. To overcome this problem, in this study, knowledge discovery and virtual tours are carried out to increase the promotion of tourism. Knowledge discovery is finding information or knowledge. Knowledge discovery uses data mining techniques to perform data analysis and find patterns. The data mining model that can be used is the frequent pattern by looking for Association Rule Mining from the data. Virtual tour is a technique that can provide 360 ??+ 180 degree images. The virtual tour will be able to show the overall environmental conditions at the tourist destination. The results that have been obtained are in the form of a quick recommendation of tourist attractions in accordance with the country of origin of tourists based on the Association rule mining values. The virtual tour has presented a 360 degree panoramic photo view to inform the environment situation in the place commented by the system.

A Novel Tourist Attraction Recommendation System Based on Improved Visual Bayesian Personalized Ranking

A Novel Tourist Attraction Recommendation System Based on Improved Visual Bayesian Personalized Ranking