摘要
文章利用马尔科夫模型和协同过滤的思想,解决了对"中国科技论文在线"用户进行实时上下文信息的动态推荐问题。先按时间排序对每一个用户的访问URL抽取,提取出状态转移矩阵,再根据协同过滤中的邻居相似度思想用余弦因子法找出最近邻的N个邻居;当给出某用户的当前访问URL时,推荐给他自身和N个最近邻居可能访问的下一个URL的集合。
In this paper, it uses Markov model and collaborative filtering ideas to solve the problem of dynamic recommendation forthe ＂Scientificpaper Online＂ user context information in real time. First, we can access to each user to extract URL according to thetime ordering, and extract the state transition matrix. Second, we use cosine factor identify the nearest neighbors of N based on ideaof the similarity neighbors in collaborative filtering. When given access to a user＇s current URL, and recommended to its and the Nnearest neighbors may visit next set of URL.
出处
《无线互联科技》
2015年第4期144-146,共3页
Wireless Internet Technology
基金
贵州省毕节市科技局
项目编号:毕科合字[2014]29号
项目名称:基于代理模型和Hadoop平台的大数据分布式数据挖掘研究
