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

Li, Hua; Su, Tao

doi:10.1155/2022/6034500

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…Pooling Layer: After feature extraction is completed in the convolutional layer, the resulting feature map is subjected to processing in the pooling layer. The main purpose of the pooling layer is to reduce the size of the feature map, thereby compressing the data and improving the network's ability to generalize [25][26][27]. This layer is also known as the downsampling layer.…”

Section: A Cnnsmentioning

confidence: 99%

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

Meng

2024

IEEE Access

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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.

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Section: A Cnnsmentioning

confidence: 99%

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

Meng

2024

IEEE Access

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“…The integration of IoT enhances the accuracy and relevance of these recommendations, ensuring alignment with the current context of the traveler. For instance, IoT-equipped wearable devices continuously collect and transmit data on user interactions (He et al 2022;Li and Su 2022). This ongoing data stream enables the recommender system to comprehend the evolving preferences and real-time needs of the traveler (Khallouki et al 2018).…”

Section: Recommender Systemsmentioning

confidence: 99%

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

Rosário,

Dias

2024

Administrative Sciences

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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.

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

mentioning

confidence: 99%

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

Computing¹

2023

Wireless Communications and Mobile Computing

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

Cited by 3 publications

References 27 publications

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

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

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

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

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