A Survey on Personalized News Recommendation Technology

In the face of massive data on the Internet, users often “lost themselves”. Personalized recommendation technology has made breakthroughs in areas such as e-commerce, advertising, audio and video recommendation in recent years. Due to the inherent characteristics of network news, such as the massive data, heterogeneity, update and change fast, timeliness and strong geographical awareness and so on, the progress of personalized recommendation technology in the field of news lags behind the above areas. And it cannot meet the requirements in news field entirely. Therefore, it is the main task of the current news recommendation system to integrate the existing personalized recommendation technologies into the news recommendation field, to study how to handle massive heterogeneous news data, to construct an optimal user preference model, and to improve the overall performance of the personalized news recommendation. This paper presents the state-of-the-art personalized news recommendation technologies in recent years, and analyzes the advantages and disadvantages of the mainstream technology based on seven main directions. Finally, the open issues in the development of personalized news recommendation technology are analyzed and concluded, hoping to guide the related work in the future.