Design And Implementation Of Tech News Recommendation System

The rapid development of information technology and Internet technology has exerted a significant impact on our daily lives.The ways people get information are various,of which online browsing news has become one of the important ways to get all kinds of information.However,due to the different needs of users,users' screening of information has become more difficult.In order to protect users from embarrassing situations of spam frenzy,user-oriented personalized recommendation service has been widely used in major sites.As personalized recommendation in e-commerce,distance education and other aspects are more and more widely used,the personalized recommendation applied to the news recommendation,is the trend.The current personalized news recommendation system is a promising business.The market provides a wide range of news and information on the site,but recommendation systems for the scientific and technological information are deficient.In vie-w of this,this article focuses on science and technology news recommendation system research and implementation.Firstly,it introduces the background and significance of the development of the subject,and analyzes the development of the personalized technology and the research status of the news recommendation system.Secondly,it analyzes and designs the system's demanding and structural function.The MVC development framework is used to design and implement the system,and the whole news page is presented in the form of a waterfall stream.The recommended module is implemented by the hybrid recommendation method.The data retrieval is carried out by the web crawler method,and the data is removed by the data cleaning technology.The content of the news is established and the user-interest model is established and updated.The ontology filtering of the recommended system is improved by using the semantic web based on the semantic network.The information network is used to solve the sparse and cold start of the system and improve the accuracy of the news recommendation.After preliminary testing,the system modules have been achieved,and the recommended effect have achieve the expectation setting.