Tourism Recommender System Research Articles

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.

The Best Tourism Recommendation Intelligent System Model: Weighted Product and K-Means Methods

Magelang Regency, with a wealth of tourist destinations with a variety of things, such as Borobudur Temple and Nepal Van Java, has extraordinary tourism potential. With this diversity, it is a challenge for tourists to determine the best tourist objects that suit their preferences. This study aims to develop an intelligent system for recommending the selection of the best tourist attractions in Magelang Regency by integrating the Weighted Product and K-Means Clustering Methods. The system is designed to provide accurate recommendations based on tourist criteria such as location, facilities, tickets, and security, as well as group attractions based on their level of potential. The Weighted Product method is used to determine the best tourist attractions, while K-Means Clustering groups tourist destinations into high, medium, and low potential categories. In this study, several stages were carried out: literature study, data collection, system design, data analysis, implementation, and system testing to produce an effective and efficient recommendation system for tourists in Magelang Regency. The results of this research obtained the best tourism, namely at Borobudur Temple.

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.

Multi-source auxiliary information tourist attraction and route recommendation algorithm based on graph attention network

Abstract In the field of tourism recommendation systems, accurately recommending scenic spots and routes for users is one of the hot research directions. In order to better consider the complex interaction between user preferences and attraction features, as well as the potential connections between different information sources, this study constructed a graph attention network model using knowledge graphs for tourist attraction and route recommendations, and extracted features from visual images using visual geometry group-16. The results indicate that, in Xian, when the learning rate is 0.01, the area under the curve value is 0.916. The area under the curve of New York is 0.909, and the learning rate is 0.001. The area under the curve value of the Tokyo dataset is 0.895. When the learning rate is moderate, the model quickly stabilizes in the first 16 rounds and reaches its optimal state in 26–30 rounds. When the propagation depth is 2, the accuracy is 0.920, 0.905, and 0.895, respectively. After introducing visual features, the accuracy, recall, and F1 score improved by 10 to 15.7%. The multi-layer perceptron further increased the effect by 4–6%. These experimental data fully demonstrate the effectiveness and accuracy of the recommendation algorithm. This study provides a powerful tool for tourism recommendation systems, which helps to further improve user experience.

Recommendation System for Sustainable Day and Night-Time Cultural Tourism Using the Mean Signed Error-Centric Recurrent Neural Network for Riyadh Historical Sites

To accommodate user-specific requirements and preferences, a travel Recommendation System (RS) gives a customized place of interest. The prevalent research did not provide solutions to some essential situations for cultural tourism, including relevant time, environmental conditions, and stay places. Thus, the existing RS models led to unreliable cultural tourism recommendations by neglecting essential factors like personalized itineraries, environmental conditions of the cultural sites, sentiment analysis of the hotel reviews, and sustainable cultural heritage planning. To overcome the above factors, a day- and night-time cultural tourism RS utilizing the Mean Signed Error-centric Recurrent Neural Network (MSE-RNN) is proposed in this paper. The proposed work develops an efficient RS by considering historical data, Geographic Information System (GIS) map location, hotel (stay place) reviews, and environmental data to access day and night cultural tourism. First, from the Geographic Information System (GIS) map and hotel data, the historical and hotel geolocations are extracted. Currently, these locations are fed to Similarity-centric Hamilton Distance-K-Means (SHD-KM) for grouping the nearest locations. Next, hotels are ranked utilizing the Tent Mapping-centric Black Widow Optimization (TM-BWO) approach centered on the locations. In addition, using Bidirectional Encoder Representations from Transformers (BERT), the essential keywords from the historical geo-locations are embedded. In the meantime, the sites’ reviews and timings are extracted from Google. The extracted reviews go through pre-processing, and the keywords from the pre-processed data are extracted. For the extracted keywords, polarity is calculated centered on the Valence-Aware Dictionary for Sentiment Reasoning (VADER). Concurrently, utilizing the Reference-centric Pearson Correlation Coefficient (R-PCC), the timings of the sites are segregated. Lastly, for providing a recommendation of tourist sites, the embedded words, ranked hotels, and segregated timings, along with the pre-processed environment and season data, are fed to the MSE-RNN classifier. At last, the experimental evaluation verified that other recommendation systems were surpassed by the proposed approach.

Modelling and Analysis of Smart Tourism Based on Deep Learning and Attention Mechanism

In the current traditional tourism recommendation systems, significant amounts of manpower and resources are required to manually identify the characteristics of resources, resulting in extremely poor economic benefits. To address this issue, this study proposes a smart tourism model based on deep learning and attention mechanisms. It uses a deep learning model to extract semantic information and improves it with the attention mechanism. This is to enable the model to take into account the complete meaning of the text and the association between individual words, thereby achieving a more comprehensive extraction of tourism resource features. The experiment showcases that the [Formula: see text]1-value of the algorithm proposed by us reached 0.961, the Recall value reached 0.958, the accuracy reached 0.980 and the area under the receiver operating characteristic curve reached 0.956. All parameters are superior to the comparison algorithm, and in practical application testing, its fitting degree reached 0.981. The above results indicate that the smart tourism proposed by us based on deep learning and attention mechanism has excellent performance in the field of tourism resource recommendation, which can effectively extract hidden features from the resources. This can also accurately push the tourism resources that users are interested in, which can effectively promote the integration and development of the tourism industry and the Internet, and has strong positive significance for economic development.

Analyzing fuzzy semantics of reviews for multi-criteria recommendations

Hotel reviews play a vital role in tourism recommender system. They should be analyzed effectively to enhance the accuracy of recommendations which can be generated either from crisp ratings on a fixed scale or real sentiments of reviews. But crisp ratings cannot represent the actual feelings of reviewers. Existing tourism recommender systems mostly recommend hotels on the basis of vague and sparse ratings resulting in inaccurate recommendations or preferences for online users. This paper presents a semantic approach to analyze the online reviews being crawled from tripadvisor.in. It discovers the underlying fuzzy semantics of reviews with respect to the multiple criteria of hotels rather than using the crisp ratings. The crawled reviews are preprocessed via data cleaning such as stopword and punctuation removal, tokenization, lemmatization, pos tagging to understand the semantics efficiently. Nouns representing frequent features of hotels are extracted from pre-processed reviews which are further used to identify opinion phrases. Fuzzy weights are derived from normalized frequency of frequent nouns and combined with sentiment score of all the synonyms of adjectives in the identified opinion phrases. This results in fuzzy semantics which form an ideal representation of reviews for a multi-criteria tourism recommender system. The proposed work is implemented in python by crawling the recent reviews of Jaipur hotels from TripAdvisor and analyzing their semantics. The resultant fuzzy semantics form a manually tagged dataset of reviews tagged with sentiments of identified aspects, respectively. Experimental results show improved sentiment score while considering all the synonyms of adjectives. The results are further used to fine-tune BERT models to form encodings for a query-based recommender system. The proposed approach can help tourism and hospitality service providers to take advantage of such sentiment analysis to examine the negative comments or unpleasant experiences of tourists and making appropriate improvements. Moreover, it will help online users to get better recommendations while planning their trips.

Hybrid Recommender System for Tourism Based on Big Data and AI

With the advancement of the Internet, technology, and communication channels, the generation of tourist data has significantly increased across various sectors such as hotels, restaurants, transportation, heritage sites, tourist events, and activities. This surge is particularly notable with the rise of Online Travel Agencies (OTAs). However, the sheer volume of options provided to tourists by web search engines or specialized tourism websites often overwhelms them, burying relevant results in a sea of information "noise." This inundation hinders or slows down the decision-making process. To alleviate this issue and aid tourists in trip planning by facilitating the discovery of relevant information, numerous recommender systems have emerged. This article offers an overview of the diverse recommendation approaches employed in the tourism domain. Through this investigation, we propose an architecture and conceptual framework for a tourism recommender system founded on a hybrid recommendation approach. This system surpasses merely suggesting a list of tourist attractions tailored to individual preferences. Instead, it functions as a comprehensive trip planner, crafting detailed itineraries comprising a variety of tourism resources, customized for specific visit durations. KeyWords: Internet Development, Tourist Data Production, Trip Planning, Conceptual Framework.

Group travel recommendation model study based on spatial and temporal integration factors

With the increasing demand for tourism, people’s travel modes are more and more diversified, and the tourism recommendation system also arises at the historical juncture. However, the current recommendation system is only recommended for a single user and does not realize the group travel recommendation. To achieve the goal of recommending its preferred attractions for multiple users, the time decay characteristics and Pearson correlation coefficient in Newton’s cooling law are used to obtain the user similarity with spatial distance factor and temporal decay factor and to obtain the score prediction results based on spatiotemporal fusion. In addition, the trust of user communication is used to recommend, and the weights of the two scoring results are added to obtain the personalized recommendation results of member users. Finally, the study used the fusion strategy to integrate the personalized recommendation results for group preference and obtained the final group travel recommendation list. Therefore, a group travel recommendation model based on spatio-temporal integration factors was constructed. According to the experimental analysis, we can see that the average HR value of the constructed model is 0.8124, and the average NDCG value is 0.7284, which can accurately judge users’ preferences and get the most suitable group travel recommendation results, thus facilitating users to make the next plan for the tourism project.

Personalized Tourist Recommender System: A Data-Driven and Machine-Learning Approach

This study introduces a data-driven and machine-learning approach to design a personalized tourist recommendation system for Nepal. It examines key tourist attributes, such as demographics, behaviors, preferences, and satisfaction, to develop four sub-models for data collection and machine learning. A structured survey is conducted with 2400 international and domestic tourists, featuring 28 major questions and 125 variables. The data are preprocessed, and significant features are extracted to enhance the accuracy and efficiency of the machine-learning models. These models are evaluated using metrics such as accuracy, precision, recall, F-score, ROC, and lift curves. A comprehensive database for Pokhara City, Nepal, is developed from various sources that includes attributes such as location, cost, popularity, rating, ranking, and trend. The machine-learning models provide intermediate categorical recommendations, which are further mapped using a personalized recommender algorithm. This algorithm makes decisions based on weights assigned to each decision attribute to make the final recommendations. The system’s performance is compared with other popular recommender systems implemented by TripAdvisor, Google Maps, the Nepal tourism website, and others. It is found that the proposed system surpasses existing ones, offering more accurate and optimized recommendations to visitors in Pokhara. This study is a pioneering one and holds significant implications for the tourism industry and the governing sector of Nepal in enhancing the overall tourism business.

Your trip, your way: An adaptive tourism recommendation system

Considerable changes in the behavior of tourists have been observed because the call for their customized experiences prevails in the market. In addition, advancements in artificial intelligence have significantly promoted the development of tourism recommendation systems (TRS), which substantially contributes in enhancing tourists’ personalized experiences. However, existing systems are facing increasing challenges in handling uncertain environments and the changing on-site behavior of tourists. This study intends to address these challenges by proposing an adaptive TRS, which mainly consists of three modules: data collection, itinerary design, and itinerary adjustment. To evaluate the proposed system’s performance, a case study was conducted in Xiamen, China. Results indicated that our system can adapt to uncertain environments and tourists’ changing on-site behavior. Tourism organizations could integrate our system in upgrading their information service platforms as part of their smart tourism construction to enhance the competitiveness of destinations.

A Decision Tree Based Recommendation System for Tourists

A Decision Tree Based Recommendation System for Tourists

Smart Tourism Recommender System Modeling Based on Hybrid Technique and Content Boosted Collaborative Filtering

Smart Tourism Recommender System Modeling Based on Hybrid Technique and Content Boosted Collaborative Filtering

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.

Toward an intelligent tourism recommendation system based on artificial intelligence and IoT using Apriori algorithm

Toward an intelligent tourism recommendation system based on artificial intelligence and IoT using Apriori algorithm

Mobile Tourism Recommender System for Users to Get a Better Choice of Tour

The system might include a turn-by-turn route highlight to prevent fake preferences that check if the user has taken the course. A larger customer overview with more participants is required to acquire more insightful client feedback. Our ex-amination was designed as a lab experiment to gather initial data straight absent. While making fun of other clients and their system comments, we looked at a few initial objective mixtures. Doing field research with actual clients using our suggested model in real-world situations (such as when looking for a course online to work from home) is crucial. This will help us better understand how effective our approach is. In this article, we developed a creative method for recommending multimodal travel routes. In a client survey with 20 participants, we evaluated the applicability of our cross-breed computation and its usability. The results show that CF, in-formation-based, and well-liked course concepts complement one more successfully than cutting-edge course organizer advances. Thanks to the Google Guides Programming interface, our application can give seven different elective trip options.

Research on User Profile Combined with Collaborative Filtering Recommendation Algorithm for Intelligent Tourism

In recent years, the online travel sector in the tourism industry has experienced significant growth and popularity due to the development and widespread adoption of internet technology and smart devices. However, despite these advancements, scenic spots have struggled to provide precise services to tourists, as the online marketplace is flooded with numerous and disorganized commodity resources, lacking standardized construction and systematic management. As a result, travelers find it challenging to access specialty goods that cater to their personalized needs. To address this issue, this paper proposes the use of user profiling and collaborative filtering recommendation algorithms to achieve personalized recommendations for specialty products in scenic spots. The general process of constructing user profiles for scenic spots and combining them with collaborative filtering algorithms to create an intelligent tourism recommendation system is outlined. The paper also highlights the current challenges faced by this system in practical applications and provides future research prospects to promote accurate services in tourist attractions.

A novel greedy genetic algorithm-based personalized travel recommendation system

In recent years, there has been a significant increase in the utilization of Tourism Recommendation Systems (TRS) to enhance tourist satisfaction. However, planning a trip can be a daunting and time-consuming process, leading to concerns for travelers. This paper focuses on developing a highly personalized TRS that considers the complexities and limitations of tour itinerary planning. To achieve this, we propose an extension of the constrained orienteering problem that selects the most suitable attractions based on various constraints, such as maximum tour duration, mandatory visits, and start and end locations. In addition, we introduce the use of tier constraints to limit the time spent on similar attractions. In this study, we also propose a novel personalization approach that considers the individual preferences of tourists and generates personalized ratings for points of interest. Next, we focus on developing a new greedy genetic algorithm to address the NP-hard problem of finding optimal or near-optimal solutions. To assess the performance of the developed algorithm, we conducted a sensitivity analysis of the input parameters. Using different user profiles, we demonstrated its effectiveness on a real-world London city dataset. Moreover, we conducted a comparative experimental analysis of the algorithm with four baseline algorithms, and the results were statistically analyzed using non-parametric tests such as Wilcoxon and Friedman. Our algorithm achieved the best-known solutions for 100% of the instances tested, demonstrating its efficiency for large-scale problems with 200-300 points of interest. Furthermore, our TRS provides highly personalized tourist trips, making it a valuable tool for tourists. The new greedy genetic algorithm and unique personalization approach are significant features of the new GlobeTrott tourist recommendation system, which is accessible on popular mobile operating systems and through the website at https://www.globetrott.com/.

Personalized Travel Recommendation Systems: A Study of Machine Learning Approaches in Tourism

Recommender systems that utilize machine learning algorithms are a prominent tool in the design and implementation of personalized tourism experiences. These systems analyze user data to generate recommendations for destinations, attractions, accommodations, and activities based on user preferences, behavior, and similarity to other users. Collaborative filtering and content-based filtering are two widely used machine learning algorithms in recommender systems, and hybrid systems that combine both approaches have shown to be effective in producing more accurate recommendations. Tourism recommendation systems (TRS) provide several benefits, including personalization, convenience, improved user experience, and increased revenue for tourism businesses. These systems can suggest destinations, attractions, accommodations, and activities that match user preferences and past behaviors, ultimately simplifying the trip planning process. Machine learning algorithms can be trained on large datasets to generate personalized recommendations, and can continuously improve their effectiveness by incorporating new data and user feedback. This paper provides a state-of-the-art overview of various types of recommendation systems (RS), including those based on user preferences, behaviors, demographic profiles, and social network judgments. The paper also presents a comparison table for these approaches. Additionally, the paper discusses the different stages of the travel process and the sources of data that can be used to develop a recommender system. The concluding section of the paper highlights the importance of personalized recommendations in the tourism industry and the potential for future research in this area.