基于点击流的用户矩阵模型相似度个性化推荐

摘要

研究用户学习网页点击流数据,挖掘用户兴趣,从而为用户进行个性化学习资源推荐,提出JMATRIX算法.基于用户历史资源点击流信息,构建用户资源点击数据有向图模型,并将有向图模型转化为矩阵模型存储.采用求解矩阵模型相似度,从而求得用户相似度,极大地降低了资源点击频率和资源点击路径用户相似度求解的复杂度,提高用户相似度求解的效率与准确度.结合Leader Clustering算法及粗糙集理论进行用户聚类和用户个性化资源推荐.实验结果表明,相比Leader Clustering算法,JMATRIX算法具有更高的效率和更准确的推荐效果.%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.