Tourism Recommendation Research Articles

Tourist Site Classification Using Keyword Matching Approach: Towards the Development of a Personality Based Tourist Recommendation System

The tourism domain consists of emotionally driven experiences between the tourists and their destinations. Psychological aspects of tourists‟ preferences are increasingly becoming an important factor to consider in the development of systems that enhance better recommendations. Studies on analysis of personality traits as well as developed systems on tourism guide/recommendation exist, however, there is less effort towards the classification of tourist sites according to personality traits and its implementation. This study, therefore, attempts to develop a tourist attraction guide through matching keywords from personality types (Big Five personality dimensions) to tourist locations in order to suggest suitable locations. Data on tourist sites were obtained from online sources with a focus on Nigeria tourist sites. The classification and matching of personality type to tourist site was done using trait keywords obtained from reviews, ratings and feedback. The tourist site information was filtered based on the personality type classification in order to deliver information about the tourist site to a user. The system prototype was implemented using HTML, CSS, JavaScript, PHP and MySQL. The resulting system is a web-based application that suggests tourist locations based on the personality preference specified by a user.

Halal Concept Package Tour Recommendation for Travel Agencies and Tour Operators: Example of Turkey

This study aims to present a model for halal concept travel agencies operating in Turkey. In this study, data were collected using qualitative research techniques. In addition, corporate web pages of related agencies will be examined. Face to face meetings with tourism academicians, theologians and customers will be held. The data obtained from the interviews will be coded and themes will be created. Halal tourism has become a common concept in recent years. People who adopt a conservative lifestyle want to make their holidays within the framework of their beliefs. Although Halal tourism is the first concept that comes to mind with halal concept, travel agencies have started to create package tours according to the wishes of conservative customers in recent years. However, there is no generally accepted halal package tour standard in this area. As a result of the study, it is envisaged to present a model halal package tour model for travel agencies and academicians. The biggest limitation of this study is that there are not too many halal concept travel agencies in the field of travel.

CAFOB: Context-aware fuzzy-ontology-based tourism recommendation system

The ever-increasing volume of information available on tourist attractions in cyberspace has made the tourist decision-making process a crucial task. Therefore, tourism recommendation systems can significantly benefit tourists in terms of comfort and satisfaction. In this paper, a context-aware fuzzy-ontology-based tourism recommendation system is proposed. In the proposed system, we have two new propositions that can be individually used in other tourism recommendation systems: a fuzzy-weighted ontology and a new sentiment/emotion score scheme. In CAFOB, the ontology-based scores of a user’s reviews are then multiplicatively modulated by sentiment/emotion scores to generate the total scores of the reviews’ ontology words, representing the user preferences. Then, the nearby-open-3+-bubbled touristic attractions’ characteristics are extracted based on their past visitors’ reviews. Finally, a recommendation list is produced using a maximum hybrid semantic similarity between the user preferences and attractions’ characteristics. The employment of contextual information, including weather, location, and time makes CAFOB context-aware and improves the accuracy and quality of the recommendations. The F-measure, NDCG, and MRR results show the outperformance of CAFOB against the state-of-the-art tourism recommendation systems for all relevant Top N recommendation options and different geographical spans of interest.

Clustering Methods Based on Stay Points and Grid Density for Hotspot Detection

With the widespread use of GPS equipment, a large amount of mobile location data is recorded, and urban hotspot areas extracted from GPS data can be applied to location-based services, such as tourist recommendations and point of interest positioning. It can also provide decision support for the analysis of population migration distribution and land use and planning. However, taxi GPS location data has a large amount of data and sparse points. How to avoid the influence of noise and efficiently detect hotspots in cities have become urgent problems to be solved. This paper proposes a clustering algorithm based on stay points and grid density. Firstly, a filtering pre-processing algorithm using stay points classification and stay points thresholds is proposed, so the influence of stop points is avoided. Then, the data space is divided into rectangular grid cells; each grid cell is determined to be a dense or non-dense grid according to the defined density threshold, and the cluster boundary points and noise points are judged in the non-dense grid cells to avoid normal sampling points being treated as noise. Finally, the associated dense grids are connected into clusters. The sampling points mapped to the grid cells are the elements in the clusters. Our method is more efficient than the DBSCAN algorithm because the grid cells are calculated. The superiority of the proposed algorithm in terms of clustering accuracy and time efficiency is verified in the real data set compared to traditional algorithms.

Fine-Grained Urban Functional Region Identification via Mobile App Usage Data

Understanding fine-grained urban function for different regions is essential for both city managers and residents in terms of strategy design, tourism recommendation, business site selection, etc. A huge amount of data from the mobile network in the past several years provides the possibility for fine-grained urban function identification since it provides the opportunity to extract useful information about urban functions. However, challenges remain: (i) there is no prior knowledge about the existence of App usage patterns relating to urban functional regions; (ii) collected data are very noisy and data from different cellular towers have different noise levels. Therefore, it is difficult to extract unique patterns to identify urban functional regions. This article proposes a fine-grained urban functional region identification system, which utilizes mobile App usage data from cellular towers. To address challenge (i), we first extract three key variables for each cellular tower, App number, user number, and traffic. Then, we design a Davies–Bouldin index (DBI)-based filtering method to automatically select the most distinguishable features for multiclassification. To address challenge (ii), we first reduce cellular tower level noise by designing a clustering-based method to select the most representative cellular tower data. The data from these cellular towers share similar patterns for the same urban functional region and different patterns between different urban functional regions. Then, we reduce feature level noise by designing a Fourier transform-based method to reconstruct the features with several key frequency components, which preserves the most important information and removes the unnecessary noise. We evaluate our system and selected features with three representative supervised learning models, all of which achieve more than 95 % classification accuracy.

Face Emotion Recognition based Recommendation System

Face recognition technology has gotten a lot of press because of its wide range of applications and market potential. It is used in a variety of fields, including surveillance systems, digital video editing, and other technical advancements. In the fields of tourism, music, video, and film, these systems have overcome the burden of irrelevant knowledge by taking into account user desires and emotional states. Advice systems, emotion recognition, and machine learning are proposed as thematic categories in the analysis. Our vision is to develop a method for recommending new content that is based on the emotional reactions of the viewers. Music is a form of art that is thought to have a stronger connection to a person's emotions. It has the unique potential to boost one's mood, and video streaming services are becoming more prevalent in people's lives, necessitating the development of better video recommendation systems that respond to their users in a customised manner. Furthermore, many users will believe that travel would be a method to help them cope with their ongoing emotions. Our project aims to create a smart travel recommendation system based on the user's emotional state. This project focuses on developing an efficient music, video, movie, and tourism recommendation system that uses Facial Recognition techniques to assess the emotion of users. The system's overall concept is to identify facial expression and provide music, video, and movie recommendations based on the user's mood.

Based on neutrosophic fuzzy environment: a new development of FWZIC and FDOSM for benchmarking smart e-tourism applications

The task of benchmarking smart e-tourism applications based on multiple smart key concept attributes is considered a multi-attribute decision-making (MADM) problem. Although the literature review has evaluated and benchmarked these applications, data ambiguity and vagueness continue to be unresolved issues. The robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy weighted with zero inconsistency (FWZIC) is proven compared with that of other MADM methods. Thus, this study extends FDOSM and FWZIC under a new fuzzy environment to address the mentioned issues whilst benchmarking the applications. The neutrosophic fuzzy set is used for this purpose because of its high ability to handle ambiguous and vague information comprehensively. Fundamentally, the proposed methodology comprises two phases. The first phase adopts and describes the decision matrices of the smart e-tourism applications. The second phase presents the proposed framework in two sections. In the first section, the weight of each attribute of smart e-tourism applications is calculated through the neutrosophic FWZIC (NS-FWZIC) method. The second section employs the weights determined by the NS-FWZIC method to benchmark all the applications per each category (tourism marketing and smart-based tourism recommendation system categories) through the neutrosophic FDOSM (NS-FDOSM). Findings reveal that: (1) the NS-FWZIC method effectively weights the applications’ attributes. Real time receives the highest importance weight (0.402), whereas augmented reality has the lowest weight (0.005). The remaining attributes are distributed in between. (2) In the context of group decision-making, NS-FDOSM is used to uniform the variation found in the individual benchmarking results of the applications across all categories. Systematic ranking, sensitivity analysis and comparison analysis assessments are used to evaluate the robustness of the proposed work. Finally, the limitations of this study are discussed along with several future directions.

Time apart while together: A smart trip design for group travelers

Family or friends traveling in a group often have different preferences and some of which may conflict with each other. Existing smart tourism recommender systems have not adequately addressed this important issue. This study attempts to tackle the problem by incorporating a “joining and forking” strategy into the system design, which allows members to have certain “time apart” to enjoy their treat separately and “time together” to co-create shared experience and memory. A comparison test was conducted based on a total of 50 groups of tourists in an island destination of Kulangsu in China. The results indicate that our new design outperforms five state-of-the-art baseline methods and realize a good balance between individual experience and co-experiences with group members.

Developing a Location-Based Recommender System Using Collaborative Filtering Technique in the Tourism Industry

The rapid growth of new information and products in the virtual environment has made it time consuming to acquire relevant information and knowledge amidst a vast amount of information. Therefore, an intelligent system that can offer the most appropriate and desirable among the large amount of information and products by following the conditions and features selected by each user should be essentially efficient. Systems that perform this task are called recommendation systems. Given the volume of social network data, challenges such as short-term processing and increased accuracy of recommendations are discussed in this type of system. Hence, it can perform processes faster with less error and can be effective in improving the performance of social recommending systems in improving the classification and clustering of information with the help of collaboration filtering methods. This study first develops an innovative conceptual model of a social network-based tourism recommendation system using Flicker network data. This model is based on 9 key components. The comparison show that the proposed method has an accuracy of 0.3% and a lower error rate.

Bibliometrix analysis of information sharing in social media

Social media has evolved at a rapid pace, influencing every aspect of the global community. The purpose of this paper is to use bibliometric analysis tools to evaluate valid works on the relationship between social media and information sharing. A review study of research papers on social media and information sharing published between 2009 and 2020 was conducted. The bibliometric analysis identified the following characteristics: (1) the primary articles are based on information sharing through social media; (2) the main authors deal with information sharing in social media; (3) the newestrend topics related to information sharing in social media are demographic characteristics, cognition, sentiment, healthcare, products and services recommendations, tourist recommendations, and COVID-19. The results show significant impact rates in studies of information sharing in social media. Overall, this study serves as a basis for new scientific questions that will contribute to the further development of this research field.

SISTEM REKOMENDASI TEMPAT WISATA MENGGUNAKAN ALGORITMA CHEAPEST INSERTION HEURISTIC DAN NAÏVE BAYES

Bali is one of the best tourism areas in Indonesia, as evidenced in 2016 Bali received a number of awards on the TripAdvisor Travelers Choice Award in global and Asian scope. However, the Corona virus outbreak from 2019, caused the tourism sector in Bali to decline, thus a solution is needed to restore the tourism sector in Bali, where one solution is to increase cultural tourism to the maximum, as the main attraction of tourist destinations in Bali. Bali. So the author proposes a tourism recommendation system, which aims to recommend tourist attractions that are suitable for tourists, which in this recommendation system is also recommended cultural tourism destinations that are directly recommended by the community, and there is also a mapping of tourist attractions as part of a tourist recommendation system, mapping of tourist attractions public and cultural attractions. In this tourism recommendation system, using the Naïve Bayes Algorithm to recommend general tourist destinations based on the personal motivation of tourists, which is based on the attributes of age, gender, natural interest, artificial interest, cultural interest of tourists, using 200 training data consisting of 14 classes of tourist attractions. . In addition, this tourist recommendation system is equipped with recommendations for routing tourist attractions using the Cheapest Insertion Heuristic Algorithm, to arrange a list of tourist attractions.
 Keywords: Recommendation System, Naïve Bayes Algorithm, Cheapest Insertion Heuristic Algorithm, Personal Motivation, Place Mapping.

A framework for recommending tourist attractions using deep learning and association rule mining-based methods

Abstract. Many of tourism recommendation researches are based on the user rating and review data on the tourism platforms, and these approaches might be only suitable for a discrete recommendation for the tourist attractions. It is because each rating and review data on the platforms is created for a tourist place, not for multiple places on a travel itinerary. A travel blog data often contains information about the multiple places on a travel itinerary, but it is difficult to analyse the data compared to the rating and review data since it is like a text document having longer text than the review. In this paper, we introduce a framework consisting of a deep learning-based tourist-attraction extraction method from the blog text and an association rule mining-based recommendation method to recommend a list of tourist attractions that might be favourable to visit together in a travel itinerary.

Retracted] Research on the Recommendation Algorithm of Rural Tourism Routes Based on the Fusion Model of Multiple Data Sources

Rural tourism has become an important force in implementing the rural revitalisation strategy and accelerating rural economic development. The hectic pace of life has made more and more city dwellers yearn for rural life, and travelling in the countryside has become their weekend choice. However, the current level of rural tourism informationization is low, the publicity is insufficient, the tourists’ awareness is low, and the source of customers is seriously insufficient. To this end, this paper proposes a relatively novel multidata source fusion tourism recommendation algorithm, which adopts the idea of tensor orthogonal decomposition and fuses multisource data models to predict the target domain’s for rating. The integrated consideration of multiple data sources under the do‐it‐yourself approach assists the target domain to discover the target user neighbourhood users more quickly and to discover the user’s interest degree more accurately. It is worth pointing out that the recommendation algorithm proposed in this paper under the fusion of multiple data sources is not necessarily applicable to data sources with weak correlation, such as travel data sources and music data sources, which are relatively weakly correlated, and the algorithm is slightly weak in making predictions of user preferences.

Intelligent recommendation of personalised tourist routes based on improved discrete particle swarm

In order to overcome the problems of low accuracy and long time consuming in traditional personalised travel route recommendation methods, this paper proposes an intelligent recommendation of personalised tourist routes based on improved discrete particle swarm. This method analyses the key problems of tourism recommendation according to the personalised tourism characteristics, collects the information of tourists' interest, and establishes the model of tourists' interest. On this basis, the discrete particle swarm optimisation algorithm is improved, and the improved discrete particle swarm optimisation algorithm is used to select the personalised travel route, and the selection results are recommended to the passengers, so as to realise the personalised travel route intelligent recommendation. The experimental results show that the recommendation accuracy of this method is between 82.5% and 96.9%, and the recommendation time is always less than 0.5 s, which can realise the accurate and rapid recommendation of personalised tourist routes.

Research on the New Mode of Smart Tourism in Zhangjiajie Based on the Big Data

With the advent of the era of big data, traditional tourism has begun to turn to smart tourism. Using big data, cloud computing and other related technologies, this paper puts forward the construction idea of smart tourism platform, which aims to strengthen the connection between tourists and tourism information, provide domestic and foreign tourists with a highly stable, safe and reliable smart tourism personalized recommendation platform—Smart travel, improve the service quality of tourism and ensure the development of tourism informatization. In view of the lack of traditional tourism information and incomplete tourism services, the collaborative filtering algorithm of the project is adopted and improved, which mainly analyzes the historical data of tourists and the interaction behavior between tourists and the system, and uses mahout to realize the distributed intelligent tourism recommendation system to provide tourists with more efficient personalized service information.

Penerapan Metode TOPSIS sebagai Rekomendasi Pemilihan Wisata (Studi Kasus: Kabupaten Cilacap)

Indonesian people have made vacation activities a primary need so that vacations are not just travelling. Now people are starting to travel longer, farther, and more often. When making tourist visits, domestic tourists are faced with many choices of destinations in the city. Cilacap City is one of the cities with many tourist attractions, in this city requires the selection of tourist objects according to the interests and interests of each tourist. So, it takes a recommendation system for the user to achieve the goal. The user can choose the desired tourist destination more effectively. The application of tourist recommendations in Cilacap City aims to overcome the problems that occur to tourists. This application uses a Decision Support System using the TOPSIS method with the development of the Agile methodology. The result implementingon of the topstopicshod in Cilacap City are the recommendation for Mount Srandil tourism by 78%, Momongan Island by 77%, Soesilo Soedarman Museum by 74%, Karang Bolong Fort by 71% and Widarapayung Beach by 63%. So, the recommended result is Srandil Mountain tourism. This application also tests the function software's functionality Blackbox method with 100% results, where users, namely tourists, can understand the application's functionality.

A novel ontology framework supporting model-based tourism recommender

<span lang="EN-US">In this paper, we present a tourism recommender framework based on the cooperation of ontological knowledge base and supervised learning models. Specifically, a new tourism ontology, which not only captures domain knowledge but also specifies knowledge entities in numerical vector space, is presented. The recommendation making process enables machine learning models to work directly with the ontological knowledge base from training step to deployment step. This knowledge base can work well with classification models (e.g., k-nearest neighbours, support vector machines, or naıve bayes). A prototype of the framework is developed and experimental results confirm the feasibility of the proposed framework.</span>

Introduction of the adaptive management system at the enterprises of the tourist sphere

Introduction. In the current situation, enterprises themselves must take responsibility for their competitiveness and market position, using all options for management systems, while stimulating the development of market relations in the field of tourism services. Businesses are required to find new, sometimes non-standard solutions and approaches. In this case, the management system should be viewed through the prism of the constant search for new adaptive forms that have focus, flexibility and diversity. The purpose of the paper is to scientifically substantiate the need for adaptive management systems and develop practical recommendations for tourism enterprises that have the potential to adapt to ever-changing operating conditions. Results. The process of adaptive management in a tourism enterprise should look like this: Step 1 – Involvement of stakeholders. Step 2 – Objectives. Step 3 – Management actions. Step 4 – models. Step 5 – monitoring plans. Step 6 – Decision Making. Step 7 – Further monitoring. Step 8 – evaluation. Step 9 – iteration – return to step 6. Thus, adaptive enterprise management is an ongoing process that requires a lot of effort and time. However, the results of its implementation should significantly improve the efficiency of the tourism enterprise. Conclusion. Adaptive management of a tourism business requires a much more open decision-making process in which stakeholders are directly involved and decision-making powers are shared between them. It also requires that the goals, assumptions and other elements of the decision-making process be clear and therefore subject to analysis and discussion. Finally, it requires a strong commitment from managers to the necessary monitoring and evaluation that underpins adaptive management, not as marginal activities but as essential elements of the process. There is no doubt that many, and perhaps most, projects in the tourism sector involve monitoring, and in some cases management actually takes into account the results of the monitoring. But this in itself is a long way from structured, adaptive decision-making in a learning-based environment.

Tourism web app with Aspect Based Sentiment Classification Framework for Tourist Review

Abstract: According to studies, current tourism recommendation systems make false recommendations that do not live up to tourist expectations. Among The majority of these systems are inefficient, which is one of the main causes of the problem. A recommendation system that incorporates user feedback element.Tourist reviews are sources of information for travellers interested in learning more about tourist destinations. Regrettably, some reviews are irrelevant, resulting in noisy statistics. Sentiment categorization algorithms based on aspects have showed potential in reducing noise. We proposed a framework for sentiment classification based on aspects that can not only detect aspects quickly but also execute classification tasks with high accuracy. The framework has been deployed to assists travellers in finding the best restaurant or lodging in a city, and its performance has been evaluated with outstanding results using real-world datasets. Keywords: Pre-processing, Classifier algorithm, Feature extraction NLP, Tourism Strategy,Machine Learning, Tourist Reviews, Aspect Based Sentiment Analysis etc.

Contrastive Trajectory Learning for Tour Recommendation

The main objective of Personalized Tour Recommendation (PTR) is to generate a sequence of point-of-interest (POIs) for a particular tourist, according to the user-specific constraints such as duration time, start and end points, the number of attractions planned to visit, and so on. Previous PTR solutions are based on either heuristics for solving the orienteering problem to maximize a global reward with a specified budget or approaches attempting to learn user visiting preferences and transition patterns with the stochastic process or recurrent neural networks. However, existing learning methodologies rely on historical trips to train the model and use the next visited POI as the supervised signal, which may not fully capture the coherence of preferences and thus recommend similar trips to different users, primarily due to the data sparsity problem and long-tailed distribution of POI popularity. This work presents a novel tour recommendation model by distilling knowledge and supervision signals from the trips in a self-supervised manner. We propose Contrastive Trajectory Learning for Tour Recommendation (CTLTR), which utilizes the intrinsic POI dependencies and traveling intent to discover extra knowledge and augments the sparse data via pre-training auxiliary self-supervised objectives. CTLTR provides a principled way to characterize the inherent data correlations while tackling the implicit feedback and weak supervision problems by learning robust representations applicable for tour planning. We introduce a hierarchical recurrent encoder-decoder to identify tourists’ intentions and use the contrastive loss to discover subsequence semantics and their sequential patterns through maximizing the mutual information. Additionally, we observe that a data augmentation step as the preliminary of contrastive learning can solve the overfitting issue resulting from data sparsity. We conduct extensive experiments on a range of real-world datasets and demonstrate that our model can significantly improve the recommendation performance over the state-of-the-art baselines in terms of both recommendation accuracy and visiting orders.