Survey of Research on Personalized News Recommendation Approaches

Survey of Research on Personalized News Recommendation Approaches

Blog Article

Personalized news recommendation is an important technology to help users obtain the news information they are interested in and alleviate information overload.In recent years, with the development of information technology and society, personalized news recommendation has been increasingly widely studied, and has achieved remarkable success in lock shock and barrel art improving the news reading experience of users.This paper aims to systematically summarize personalized news recommendation methods based on deep learning.Firstly, it introduces personalized news recommendation methods and analyzes their characteristics and influencing factors.

Then, the overall framework of personalized news recommendation is given, and the personalized news recommendation methods based on deep learning are analyzed and summarized.On this basis, it focuses on personalized news recommendation methods based on graph structure learning, including user-news interaction graph, knowledge graph and social relationship graph.Finally, it analyzes the challenges of the current personalized news recommendation, discusses how to solve the problems of data sparsity, model interpretability, diversity of recommendation results and news privacy protection in personalized news recommendation system, and puts forward more specific and operable research ideas in the here future research direction.