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- Title
A Survey of Personalized News Recommendation.
- Authors
Meng, Xiangfu; Huo, Hongjin; Zhang, Xiaoyan; Wang, Wanchun; Zhu, Jinxia
- 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.
- Subjects
KNOWLEDGE graphs; INFORMATION overload; RECOMMENDER systems; PREDICTION models; USER experience; ELECTRONIC newspapers
- Publication
Data Science & Engineering, 2023, Vol 8, Issue 4, p396
- ISSN
2364-1185
- Publication type
Article
- DOI
10.1007/s41019-023-00228-5