计算机工程2018，Vol.44Issue(1)：219-225,7.DOI:10.3969/j.issn.1000-3428.2018.01.037
基于点击流的用户矩阵模型相似度个性化推荐
Similarity Personalized Recommendation of User Matrix Model Based on Click Stream
姜宇 1张大方 2刁祖龙1
作者信息
- 1. 湖南大学信息科学与工程学院,长沙410082
- 2. 湖南大学可信系统与网络实验室,长沙410082
- 折叠
摘要
Abstract
In order to research the web page click stream data of user's,mining user interest to recommend personalized learning resources for them,this paper proposes the JMATRIX algorithm.Based on the user's historical resources click stream information,setting up the directed-graph model of user's resources click data,and transforming the directed-graph model into matrix model to store.By solving the similarity of matrix model,to obtain the similarity of users,it greatly reduced the complexity of solving user's similarity of resource click frequency and resource click path,and improved the efficiency and accuracy of the user's similarity.Combining the Leader Clustering algorithm and rough set theory to realize the user personalized resources recommendation.Experimental results show that the JMATRIX algorithm has higher efficiency and more accurate recommendation effect compared to Leader Clustering algorithm.关键词
点击流/有向图/用户相似度/用户聚类/个性化推荐Key words
click stream/directed graph/user similarity/user clustering/personalized recommendation分类
信息技术与安全科学引用本文复制引用
姜宇,张大方,刁祖龙..基于点击流的用户矩阵模型相似度个性化推荐[J].计算机工程,2018,44(1):219-225,7.
