摘要
提出了基于主题聚类的Web资源个性化推荐算法PRWRTC(personalized recommendations of Web resource based on Topic clustering),该算法首先基于Web资源的主题提出Web资源聚类算法,从而提高Web资源聚类的精确度,进而提升Web资源推荐的准确度;然后,基于用户的浏览行为,提出实时获取用户偏好的算法;最后,针对用户偏好的动态演化,在算法中加入了时效的概念,实现了对Web资源的动态推荐.并通过实验验证了该算法的有效性.
The PRWRTC(personalized recommendations of Web resource based on Topic clustering)algorithm is proposed in this paper.The Web resources based on the theme of Web resource which can improve the accuracy of Web resources clustering,and can enhance the accuracy of recommended Web resources.On the basis of the user's browsing behavior,the algonithm that gets users′preferences in real-time is given out.According to the dynamic evolution of user preferences,the concept of efficiency is put into the algorithm,and the dynamic recommendation of Web resources realized.And experiments verify the effectiveness of the algorithm.
出处
《微电子学与计算机》
CSCD
北大核心
2015年第4期35-39,共5页
Microelectronics & Computer
基金
国家自然科学基金项目(61272111
61202031
61273216
61202032)
湖北省自然科学基金项目(2013CFB002
2013CFA115)
武汉市科技攻关技术项目(201210621214
201210421132)
关键词
语义网
主题
隐式跟踪
个性化推荐
Semantic Web
theme
implicit tracking
personalized recommendations
