随着当前互联网的快速发展，各个领域的数据量开始了爆炸式增长，“信息过载

随着当前互联网的快速发展，各个领域的数据量开始了爆炸式增长，“信息过载”是互联网领域不得不面对的问题。如今，资讯行业已正式进入在泛资讯时代，用户开始更加偏好“我感兴趣的”和“我关心的”信息，因此个性化推荐已成为资讯、新闻类客户端提升服务质量、吸引受众的主要手段，也是新闻媒体行业发展的必然趋势。在此背景下，本文采用大数据处理技术和机器学习推荐算法，以新闻内容数据、用户行为日志数据、用户基础数据为基础，对基于用户画像的新闻个性化推荐方法进行了研究，主要研究内容有：

With the current rapid development of the Internet, the amount of data in various fields began to grow explosively. "Information overload" is a problem that the Internet field has to face. Nowadays, the information industry has officially entered the pan information era, and users begin to prefer "what I am interested in" and "what I care about". Therefore, personalized recommendation has become the main means for information and news clients to improve service quality and attract audiences, and it is also the inevitable trend of the development of the news media industry. In this context, using big data processing technology and machine learning recommendation algorithm, this paper studies the personalized news recommendation method based on user portrait based on news content data, user behavior log data and user basic data. The main research contents are as follows:

With the rapid development of the Internet, the amount of data in various fields has started to explode, and "information overload" is a problem that the Internet field has to face. Nowadays, the information industry has officially entered the era of pan-information, and users begin to prefer the information that I am interested in and that I care about. Therefore, personalized recommendation has become the main means for information and news clients to improve service quality and attract audiences, and it is also the inevitable trend of news media industry development. Under this background, this paper adopts big data processing technology and machine learning recommendation algorithm, based on news content data, user behavior log data and user basic data, studies the personalized news recommendation method based on user portrait, and the main research contents are as follows: