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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Bibliographic Details
Published in:Data Science and Engineering Vol. 8; no. 4; pp. 396 - 416
Main Authors: Meng, Xiangfu, Huo, Hongjin, Zhang, Xiaoyan, Wang, Wanchun, Zhu, Jinxia
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.12.2023
Springer
Springer Nature B.V
SpringerOpen
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2364-1185
2364-1541
DOI:10.1007/s41019-023-00228-5