A Survey of Personalized News Recommendation

被引：4
|
作者：
Meng, Xiangfu [1 ]
Huo, Hongjin [1 ]
Zhang, Xiaoyan [1 ]
Wang, Wanchun [1 ]
Zhu, Jinxia [1 ]
机构：
[1] Liaoning Tech Univ, Sch Elect & Informat Engn, Liaoning 125105, Peoples R China
来源：
DATA SCIENCE AND ENGINEERING | 2023年 / 8卷 / 04期
基金：
美国国家科学基金会;
关键词：
Personalized news recommendation; Prediction models; News ranking and display; Graph structure learning;
D O I：
10.1007/s41019-023-00228-5
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：396 / 416
页数：21
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