A Survey of Personalized News Recommendation

Personalized news recommendation is an important technology to help users obtain news information they are interested in and alleviate information overload. In recent years, news recommendation has been increasingly widely studied and has achieved remarkable success in improving the news reading exp...

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Veröffentlicht in:	Data Science and Engineering Jg. 8; H. 4; S. 396 - 416
Hauptverfasser:	Meng, Xiangfu, Huo, Hongjin, Zhang, Xiaoyan, Wang, Wanchun, Zhu, Jinxia
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 01.12.2023 Springer Springer Nature B.V SpringerOpen
Schlagworte:	Algorithm Analysis and Problem Complexity Algorithms Artificial Intelligence Chemistry and Earth Sciences Computer Science Customization Data Mining and Knowledge Discovery Database Management Deep learning Graph structure learning Knowledge representation Neural networks News News ranking and display Personalized news recommendation Physics Prediction models Reading Recommender systems Review/Survey Papers Semantics Social networks Statistics for Engineering Systems and Data Security User behavior China Graph structure learning Personalized news recommendation Prediction models News ranking and display
ISSN:	2364-1185, 2364-1541
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Abstract	Personalized news recommendation is an important technology to help users obtain news information they are interested in and alleviate information overload. In recent years, news recommendation has been increasingly widely studied and has achieved remarkable success in improving the news reading experience of users. In this paper, we provide a comprehensive overview of personalized news recommendation approaches. Firstly, we introduce personalized news recommendation systems according to different needs and analyze the characteristics. And then, a three-part research framework on personalized news recommendation is put forward. Based on the framework, the knowledge and methods involved in each part are analyzed in detail, including news datasets and processing techniques, prediction models, news ranking and display. On this basis, we focus on news recommendation methods based on different types of graph structure learning, including user–news interaction graph, knowledge graph and social relationship graph. Lastly, the challenges of the current news recommendation are analyzed and the prospect of the future research direction is presented.
AbstractList	Personalized news recommendation is an important technology to help users obtain news information they are interested in and alleviate information overload. In recent years, news recommendation has been increasingly widely studied and has achieved remarkable success in improving the news reading experience of users. In this paper, we provide a comprehensive overview of personalized news recommendation approaches. Firstly, we introduce personalized news recommendation systems according to different needs and analyze the characteristics. And then, a three-part research framework on personalized news recommendation is put forward. Based on the framework, the knowledge and methods involved in each part are analyzed in detail, including news datasets and processing techniques, prediction models, news ranking and display. On this basis, we focus on news recommendation methods based on different types of graph structure learning, including user–news interaction graph, knowledge graph and social relationship graph. Lastly, the challenges of the current news recommendation are analyzed and the prospect of the future research direction is presented. Abstract Personalized news recommendation is an important technology to help users obtain news information they are interested in and alleviate information overload. In recent years, news recommendation has been increasingly widely studied and has achieved remarkable success in improving the news reading experience of users. In this paper, we provide a comprehensive overview of personalized news recommendation approaches. Firstly, we introduce personalized news recommendation systems according to different needs and analyze the characteristics. And then, a three-part research framework on personalized news recommendation is put forward. Based on the framework, the knowledge and methods involved in each part are analyzed in detail, including news datasets and processing techniques, prediction models, news ranking and display. On this basis, we focus on news recommendation methods based on different types of graph structure learning, including user–news interaction graph, knowledge graph and social relationship graph. Lastly, the challenges of the current news recommendation are analyzed and the prospect of the future research direction is presented.
Audience	Academic
Author	Wang, Wanchun Zhu, Jinxia Zhang, Xiaoyan Meng, Xiangfu Huo, Hongjin
Author_xml	– sequence: 1 givenname: Xiangfu surname: Meng fullname: Meng, Xiangfu organization: School of Electronic and Information Engineering, Liaoning Technical University – sequence: 2 givenname: Hongjin surname: Huo fullname: Huo, Hongjin email: hhj991009@163.com organization: School of Electronic and Information Engineering, Liaoning Technical University – sequence: 3 givenname: Xiaoyan surname: Zhang fullname: Zhang, Xiaoyan organization: School of Electronic and Information Engineering, Liaoning Technical University – sequence: 4 givenname: Wanchun surname: Wang fullname: Wang, Wanchun organization: School of Electronic and Information Engineering, Liaoning Technical University – sequence: 5 givenname: Jinxia surname: Zhu fullname: Zhu, Jinxia organization: School of Electronic and Information Engineering, Liaoning Technical University
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Snippet	Personalized news recommendation is an important technology to help users obtain news information they are interested in and alleviate information overload. In... Abstract Personalized news recommendation is an important technology to help users obtain news information they are interested in and alleviate information...
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SubjectTerms	Algorithm Analysis and Problem Complexity Algorithms Artificial Intelligence Chemistry and Earth Sciences Computer Science Customization Data Mining and Knowledge Discovery Database Management Deep learning Graph structure learning Knowledge representation Neural networks News News ranking and display Personalized news recommendation Physics Prediction models Reading Recommender systems Review/Survey Papers Semantics Social networks Statistics for Engineering Systems and Data Security User behavior
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Title	A Survey of Personalized News Recommendation
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