基于内容推荐的新闻推荐策略优化和实现研究

Research on Optimization and Implementation of News Recommendation Strategy Based on Content Recommendation

随着互联网和信息技术的高速发展,社会进入信息爆炸时期,在新闻领域,网络新闻已经成为人们获取信息的重要途经,但是人们只能被动接受新闻网站提供的新闻。如何从海量的新闻信息中筛选用户感兴趣的新闻内容推荐给用户已经成为各大互联网公司共同面临的难题之一。为了有效解决这一问题,推荐技术应运而生。研究开发个性化新闻推荐模型,并且对传统基于内容推荐的模型进行改进,按照用户的自定义标签或者历史阅读数据把用户关注领域的新闻或热度新闻推荐给用户。

With the rapid development of Internet and information technology,the society has entered the period of information explosion.In the field of news,network news has become an important way for people to obtain information,but people can only passively accept the news provid⁃ed by news websites.How to select the news content that users are interested in and recommend it to users has become one of the common problems faced by the major Internet companies.In order to solve this problem effectively,recommendation technology came into being.This paper mainly studies and develops the personalized news recommendation model,and improves the traditional content-based recom⁃mendation model.According to the user-defined tags or historical reading data,the news or hot news in the user's field of concern is recom⁃mended to the user.