个性化新闻推荐系统研究综述及探讨OACSTPCD

With the rapid development of news media technology and the exponential growth of the number of online news,per-sonalised news recommendation plays an extremely crucial role in order to solve the problem of online information overload.It learns users'browsing behaviour,interests and other information,and actively provides user with news of interest,thus improv-ing user's reading experience.Personalised news recommendation has become a hot research and practical problem in the field of journalism and computer science,and experts in the industry have proposed various recommendation algorithms to improve the performance of recommendation systems.In this paper,we systematically describe the latest research status and progress of per-sonalised news recommendation.firstly,we briefly introduce the architecture of news recommendation systems,and then we study the key recommendation algorithms and common evaluation metrics in news recommendation systems.Although person-alised news recommendation brings a good experience to users,it also brings a lot of unknown effects to users.Unlike other news recommendation reviews,this paper also examines the impact of current news recommendation systems on user behaviour and the problems they face.Finally,the paper proposes research directions and future work on personalised news recommendation based on the current problems encountered.