A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph

Shao, Bilin; Li, Xiaojun; Bian, Genqing

doi:10.1016/j.eswa.2020.113764

Cited by 101 publications

(43 citation statements)

References 92 publications

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“…User profiling is a big data processing method for personalized services in the big data environment [9][10][11][12]. rough the mining and refining of massive user data, we can obtain a full picture of users, profile them from different dimensions such as background, characteristics, personality, behavior, and scenario, and connect their needs with services.…”

Section: User Profiling and Personalized Smart Services For Librariesmentioning

confidence: 99%

Personalized Intelligent Recommendation Algorithm Design for Book Services Based on Deep Learning

Yang

2022

Wireless Communications and Mobile Computing

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Machine learning is one of the important branches of artificial intelligence, which provides new technical means for analyzing users, understanding users, and gaining insight into users. The framework of the personalized intelligent service model for libraries based on user portraits is proposed, and intelligent technologies such as machine learning are applied to analyze and mine users’ big data, build user portraits, associate users and resources with user portraits, and provide personalized intelligent services for users. The study gives a case study of book recommendation by firstly extracting users’ personalized interests, then applying the plain Bayesian algorithm to discover users’ current personalized potential demands for books, and finally providing users with personalized intelligent book recommendation services that match their interests and mainly meet their potential demands.

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Section: User Profiling and Personalized Smart Services For Librariesmentioning

confidence: 99%

Personalized Intelligent Recommendation Algorithm Design for Book Services Based on Deep Learning

Yang

2022

Wireless Communications and Mobile Computing

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“…It reflects the amount of information contained in a random variable with respect to other variables Y. Assuming that the marginal probability density function of variable X is P(x), the marginal probability density function of variable Y is P(y), and their joint probability density function is calculated to be P(x, y), the value of mutual information is calculated as shown in Equation (1).…”

Section: Time-related Mrmr Feature Selectionmentioning

confidence: 99%

“…For a long time, network security has been a concern [1,2]. Network security plays a key role in maintaining the security, economic development, and social stability of each country [3][4][5].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning

et al. 2022

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One of the foundational and key means of optimizing network service in the field of network security is traffic identification. Various data transmission encryption technologies have been widely employed in recent years. Wrongdoers usually bypass the defense of network security facilities through VPN to carry out network intrusion and malicious attacks. The existing encrypted traffic identification system faces a severe problem as a result of this phenomenon. Previous encrypted traffic identification methods suffer from feature redundancy, data class imbalance, and low identification rate. To address these three problems, this paper proposes a VPN-encrypted traffic identification method based on ensemble learning. Firstly, aiming at the problem of feature redundancy in VPN-encrypted traffic features, a method of selecting encrypted traffic features based on mRMR is proposed; secondly, aiming at the problem of data class imbalance, improving the Xgboost identification model by using the focal loss function for the data class imbalance problem; Finally, in order to improve the identification rate of VPN-encrypted traffic identification methods, an ensemble learning model parameter optimization method based on optimal Bayesian is proposed. Experiments revealed that our proposed VPN-encrypted traffic identification method produced more desirable VPN-encrypted traffic identification outcomes. Meanwhile, using two encrypted traffic datasets, eight common identification algorithms are compared, and the method appears to be more accurate in identifying encrypted traffic.

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“…Moreover, bibliometrics provides author keyword analysis to provide better current or future research trends, reflecting the recent trends in a particular field [23,24]. Many scholars use bibliometrics to study the development status, hot issues, or future research directions of a topic, such as the field of neuroscience [25] and recommendation systems [26]. Even more exciting topics, for example, multiple risks [27] and emerging interest in water research and collaboration [28], are analyzed through bibliometrics.…”

Section: Introductionmentioning

confidence: 99%

Bibliometric Analysis of the Blockchain Scientific Evolution: 2014–2020

Luo

Bai

2021

IEEE Access

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As one of the disruptive technologies in the fourth industrial revolution, Blockchain has irrevocably changed how people live and work. Many scholars have studied the Blockchain, and the number of related publications has increased over time. Bibliometrics is an excellent method to study the development trend of a specific field. However, few scholars have used bibliometrics to research Blockchain; the inheritance and evolution among its different research themes have not yet been fully understood. This study emphasizes the scientific evolution and research focus of Blockchain through bibliometric analysis. We analyzed 2993 publications from 2014 to 2020 using co-word analysis and cluster analysis with bibliometric tools such as the SciMAT. The most productive author, country, institutions and leading research areas were identified, outlining the Blockchain field's fundamental developments. The results show the changes in keywords regarding Blockchain summarize the research hotspots in different periods. The study discusses the evolution process of various themes in each period, providing a meaningful reference for scholars to identify research hotspots and future research directions in the Blockchain field.

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A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph

Cited by 101 publications

References 92 publications

Personalized Intelligent Recommendation Algorithm Design for Book Services Based on Deep Learning

Personalized Intelligent Recommendation Algorithm Design for Book Services Based on Deep Learning

A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning

Bibliometric Analysis of the Blockchain Scientific Evolution: 2014–2020

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