This study explores the transformative impact of IoT technologies on smart tourism, striving to boost operational efficiency and enrich the traveler experience. Using a systematic literature review with bibliometric analysis, we examined a sample of 83 studies indexed in SCOPUS to identify research activity on this topic until November 2023. The integration of cutting-edge technologies, including big data, smart sensors, cloud computing, machine learning, artificial intelligence, wearables, mobile applications, augmented reality, and virtual reality, establishes the foundation of the IoT-enabled smart tourism ecosystem. These innovations bring distinct capabilities, from facilitating data collection with embedded sensor technologies to using cloud computing for essential services like data storage and analytics. Examining various IoT applications in smart tourism, such as recommender systems, smart cities, payment systems, and electronic ticketing, reveals their positive effects on safety, efficiency, and personalized services. However, challenges like security, privacy, software complexity, scalability, and interoperability necessitate robust measures. This study identifies future research directions, including enhancing security and privacy, exploring blockchain integration, investigating edge computing, and improving interactions between tourists and smart tourism systems. These endeavors aim to address challenges and seize opportunities, fostering innovative solutions for the evolving needs of the tourism industry in a technologically advanced landscape.

Exploring the Landscape of Smart Tourism: A Systematic Bibliometric Review of the Literature of the Internet of Things

The relentless progression of advanced technologies has driven the seamless integration of Internet of Things (IoT) services into the fundamental framework of contemporary tourism enterprises. In the quest for valuable insights from the vast reservoir of tourism data, this study employs a Convolutional Neural Network (CNN) as its primary instrument, culminating in the development of a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model designed for the classification of textual data in the smart tourism domain. The primary function of this model is to conduct sentiment analysis on smart tourism data, specifically by categorizing review data into either positive or negative sentiments. The incorporation of review ratings further aids in the accurate labeling of data according to their respective sentiment categories, thus streamlining the process of effective data annotation. The empirical findings reveal distinctive performance trajectories for the three models concerning positive comments across various evaluation metrics. Remarkably, in terms of precision, the CNN-BiLSTM model leads with an impressive 87.9%, closely followed by the BiLSTM model at 87.4%, while the Text Convolutional Neural Network (TextCNN) model trails slightly at 85.3%. Similarly, the recall highlights the CNN-BiLSTM model’s excellence, achieving an impressive 88.34%, compared to 78.5% for BiLSTM and 77.6% for TextCNN models. In the realm of accuracy, the CNN-BiLSTM model maintains its dominance at 85.1%, while the BiLSTM and TextCNN models achieve 82.7% and 81.4%, respectively. Notably, the CNN-BiLSTM model outperforms the trio in terms of the F1 value, securing a robust 85.41%. In summary, the CNN-BiLSTM model consistently demonstrates excellent performance across a range of metrics, including precision, recall, accuracy, and F1 value, signifying its supremacy in this classification task. This study presents a systematic solution for enhancing smart tourism services, thereby providing a strong foundation for the growth and advancement of tourism enterprises.

The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things

. With the advent of the mass tourism era, innovative tourism services have attracted increasing attention from numerous tourism consumers. The wide application of big data and cloud computing technology makes it possible to build an intelligent tourism service platform to improve the experience of tourists. This research proposes a series of building technical proposals for a smart tourism service platform based on big data technology. The platform mainly focuses on massive data-based tourism administration, such as emotion analysis, tourist route recommendation, tourist portrait, passenger ﬂow trend prediction, using big data analysis, mining, and processing. It also produces a series of relevant services and constructs special applications with various functions to realize tourism data acquisitions, research, and manipulations. Finally, the intelligent tourism platform’s massive data analysis and processing results provide feasible and opti-mized decision-making references for the government, enterprises, tourists, and other stakeholders. It puts forward countermeasures for the innovative tourism service platform’s implementation and application to promote the sustainable development of creative tourism.

Research on the Construction and Application of Smart Tourism Platforms Based on Big Data Technology

Retracted: SM-PageRank Algorithm-Based User Interest Model for Mobile Smart Tourism Platform

Smart tourism, also known as smart tourism, actively captures tourism activities, tourists, tourism economy, tourism resources, and other information through mobile Internet and mobile terminal Internet of things devices and emerging technologies such as cloud computing and Internet of things. In order to release the intelligent tourism information in time, let the masses know the information in time, and adjust the work and tourism plan in time, this paper proposes SM-PageRank algorithm and secondary ranking based on user interest model, in order to study the accuracy of tourism information retrieval. The methods used in this paper include the principle of three weighted information fusion algorithms, LBS technology, and the design of intelligent tourism system. The function of information fusion algorithm is to find the global optimal solution for travel routing. LBS technology collects real-time tourism information through some entity sensors. Through information retrieval experiment and fusion technology solution experiment, the results show that the SM-PageRank algorithm and the secondary sorting based on user interest model proposed in this paper improve the average accuracy by 20.1% compared with the traditional algorithm and 2.6% compared with Google search. The Internet of things fusion algorithm gives a line planning set with standard deviation of 0.4 for the set of travel days with standard deviation of 1.92.

/pdf/sm-pagerank-algorithm-based-user-interest-model-for-mobile-l4q0uehp.pdf

SM-PageRank Algorithm-Based User Interest Model for Mobile Smart Tourism Platform

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SM-PageRank Algorithm-Based User Interest Model for Mobile Smart Tourism Platform