  Due to researchers'aim to study personalized recommendations for different
business fields, the summary of recommendation methods in specific fields is of
practical significance. News recommendation systems were the earliest research
field regarding recommendation systems, and were also the earliest
recommendation field to apply the collaborative filtering method. In addition,
news is real-time and rich in content, which makes news recommendation methods
more challenging than in other fields. Thus, this paper summarizes the research
progress regarding news recommendation methods. From 2018 to 2020, developed
news recommendation methods were mainly deep learning-based, attention-based,
and knowledge graphs-based. As of 2020, there are many news recommendation
methods that combine attention mechanisms and knowledge graphs. However, these
methods were all developed based on basic methods (the collaborative filtering
method, the content-based recommendation method, and a mixed recommendation
method combining the two). In order to allow researchers to have a detailed
understanding of the development process of news recommendation methods, the
news recommendation methods surveyed in this paper, which cover nearly 10
years, are divided into three categories according to the abovementioned basic
methods. Firstly, the paper introduces the basic ideas of each category of
methods and then summarizes the recommendation methods that are combined with
other methods based on each category of methods and according to the time
sequence of research results. Finally, this paper also summarizes the
challenges confronting news recommendation systems.
