User clustering analysis based on implicit navigation
基于隐式浏览输入的用户聚类分析

Started in the sparse matrix of recommender systems, and ensured the recommended coverage rate. Based on the idea of sociality density, improved the accuracy by matrix reduction. With the user ratings matrix which had been normalized, transformed the consistency score matrix. Meanwhile, with the concept of sociality, got an aggregation analysis result based on user similarity score preferences through adjusting the matrix. Computed the sociality densities by constructing a sociality network diagram. Selected the maximum as the cluster centers, and used the compact and separation effect functions to validate the reliability of aggregation results. Compared to other method, the method reduces the computational complexity. Meanwhile, with the test of Movielens, the method is accuracy and effectiveness in a certain degree.