Content Recommendation Algorithm for Intelligent Navigator in Fog Computing Based IoT Environment

Lin, Fuhong; Zhou, Yutong; You, Ilsun; Lin, Jiuzhi; An, Xueli; Xing, Lu

doi:10.1109/access.2019.2912897

Cited by 28 publications

(11 citation statements)

References 40 publications

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“…The core of Spring Boot framework is automatic configuration. Using common frameworks in zero XML configuration greatly simplifies the development mode, improves the development efficiency, and shortens the development cycle of the project (Lin et al, 2019). Therefore, in this system, Spring microservice architecture is adopted to decompose the huge service into multiple microservices.…”

Section: Artificial Intelligence Microservice Architecture Based On Deep Learning and Spring Cloud Methodsmentioning

confidence: 99%

Design and Implementation of Intelligent Sports Training System for College Students' Mental Health Education

Wang

Park

2021

Front. Psychol.

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In order to solve the problems of poor physical fitness of college students and low efficiency of college sport venues' management, an intelligent sports management system based on deep learning technology is designed by using information technology and human-computer interaction under artificial intelligence. Based on the Browser/Server (B/S) structure, the intelligent sports management system is constructed. The basic framework of Spring Cloud is used to integrate the framework and components of each part, and a distributed microservice system is built. The artificial intelligence recommendation algorithm is used to analyze the user's age, body mass index (BMI), and physical health status, and recommend sports programs suitable for students, thus realizing the intelligent sports program recommendation function. At the same time, the recommendation algorithm is used to complete the course recommendation according to the students' preferences, teaching distance, opening time, course evaluation, and other indexes, and the course registration system is constructed; after the analysis of the entity and the relationship between the entities of the intelligent sports system, the database relational model of the system is designed with the entity relationship (E-R) diagram. The results of the functional test show that the system can run well. In conclusion, the sports training environment instructional system based on artificial intelligence and deep learning technology can meet the teaching needs of colleges, improve the sports' quality for college students, and promote psychological education.

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Section: Artificial Intelligence Microservice Architecture Based On Deep Learning and Spring Cloud Methodsmentioning

confidence: 99%

Design and Implementation of Intelligent Sports Training System for College Students' Mental Health Education

Wang

Park

2021

Front. Psychol.

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“…User identification and profile aggregation and merging are performed before generating recommendation strategy. Reference [74] proposed a weighted interest degree recommendation algorithm using association rules for intelligence in the IoV. User interest score is predicted by establishing an association between user interests and recommend personalized service.…”

Section: Vehicle Content Recommendationmentioning

confidence: 99%

Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

Luo

et al. 2020

Intell. and Converged Netw.

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Internet of Vehicles (IoV) is a distributed network of connected cars, roadside infrastructure, wireless communication networks, and central cloud platforms. Wireless recommendations play an important role in the IoV network, for example, recommending appropriate routes, recommending driving strategies, and recommending content. In this paper, we review some of the key techniques in recommendations and discuss what are the opportunities and challenges to deploy these wireless recommendations in the IoV.

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“…Intelligent navigator is a key component in electric vehicle for suitable human-computer interaction. In [70], a weighted interest degree recommendation algorithm is proposed by using association rules to improve the accuracy of recommendation content. Moreover, the recommender systems can reduce the time of search and screening the massive retrieval of information.…”

Section: B Electric Vehiclementioning

confidence: 99%

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Sun

et al. 2020

IEEE Access

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Under the explosive growth of information available on the Web, recommender systems have been used as an effective technology to filter useless information and attempt to recommend the most useful items. The proliferation of smart phones, smart wearable devices and other Internet of Thing (IoT) devices has gradually driven many novel emerging services which are latency-sensitive and computation-intensive with a higher quality-of-service. Under such circumstances, the data sources contain four key characteristics (i.e., sparsity, heterogeneity, mobility, volatility). The conventional recommender systems based on cloud computing are incapable of digging the information of user demands. Mobile edge computing is a novel computing paradigm via pushing computation/storage resource from the remote cloud servers to the network edge servers to provide more intelligent and personalized service. This paper comprehensively reviews the state of the art literature on the convergence of recommender systems and edge computing, and identify the future directions along this dimension. This paper can provide an array of new perspectives on the convergence for researchers, practitioners, and tap into the richness of this interdisciplinary research area.

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Content Recommendation Algorithm for Intelligent Navigator in Fog Computing Based IoT Environment

Cited by 28 publications

References 40 publications

Design and Implementation of Intelligent Sports Training System for College Students' Mental Health Education

Design and Implementation of Intelligent Sports Training System for College Students' Mental Health Education

Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

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