基于用户关注度的个性化推荐系统研究

Research on Personalized Recommendation System Based on User's Attention

传统推荐系统存在不以用户关注度为导向、推荐信息分散、推荐效率低等弊端,针对该问题,通过深入挖掘用户的关注度特征及浏览行为特征,分析用户的潜在需求,以此为基础,综合基于类别关注度的信息推荐、基于主题词的长期关注度推荐以及基于协同过滤的个性化推荐算法,采用模块化的设计方法,提出一种基于用户关注度的个性化推荐系统。实践应用表明,该系统能够帮助用户从海量信息中快速、准确地找到自己关注的内容,对互联网个性化信息服务具有一定的应用价值。

The traditional recommendation system bears the disadvantages of lack of user＇s attention,recommendation information dispersion,low recommendation efficiency,etc.In view of the problems,the system analyzes user＇s potential requirements through in-depth mining of user attention characteristics and browsing behavior characteristics.Information recommendation based on category concern degree,long-term attention recommendation based on keywords and personalized recommendation algorithm based on collaborative filtering are integrated;the personalized recommendation system based on user attention in modular designing is proposed.It is proved that the system can help users find their own contents quickly and accurately from massive information and has application value in Internet personalized information service.