个性化新闻推荐系统研究综述及探讨  

Review and Discussion of Personalised News Recommendation Systems

作　　者：翟梅 ZHAI Mei(School of Journalism and Communication,Shaanxi Normal University,Xi’an 710119,China)

机构地区：[1]陕西师范大学新闻与传播学院,陕西西安710119

出　　处：《计算机与现代化》2024年第4期12-20,共9页Computer and Modernization

基　　金：国家自然科学基金资助项目(61901250)。

摘　　要：随着新闻媒体技术的快速发展,网络新闻数量呈指数级增长。为了解决网络信息过载的问题,个性化新闻推荐扮演着极其关键的角色。它通过学习用户的浏览行为、兴趣爱好等信息,主动为用户提供感兴趣的新闻,从而提高用户的阅读体验。个性化新闻推荐逐渐成为新闻领域及计算机科学领域的研究热点和实践难题,业界专家已提出多种推荐算法用于提高推荐系统的性能。本文系统阐述个性化新闻推荐的国内外最新研究现状和进展,首先,简要介绍新闻推荐系统的架构,并对新闻推荐系统中核心推荐算法和常用评价指标进行研究。虽然个性化新闻推荐给用户带来很好的体验,但是潜移默化中也给用户带来很多未知的影响。跟其他新闻推荐综述不同的是,本文还结合新闻媒体专业研究了当前新闻推荐系统对用户行为产生的影响及面临的问题。最后,根据当前遇到的问题提出个性化新闻推荐的研究方向及未来工作重点。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.

关 键 词：推荐系统 用户个性化 新闻推荐 用户行为 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]