基于上下文多维度的移动用户偏好动态分析方法

摘要

Because contextual data ins introduced into dynamic analysis of mobile Users preference,the original userproject two-dimensional matrix will be extended to users-Projects-Context three dimensional matrix decomposition theory based on multi-dimensional matrix of low rank,which can simplify the analysis of the data,but the low rank decomposition properties of self-learning method for a mobile user preferences dynamic analysis do not take full advantage of multi-dimensional matrix.To solve this problem,this paper presented a self-learning method which uses multi-dimensional matrix of low rank decomposition,promoting the convergence rate,reducing the data analysis complexity.The simulation results show the effectiveness of the proposed algorithm.%移动用户偏好的动态分析由于引入了上下文数据,使得原有的用户-项目二维矩阵将扩展为用户-项目-上下文的三维矩阵.根据多维矩阵中低秩分解理论,可以简化数据的分析,但是其移动用户偏好动态分析的自学习方法没有充分利用多维矩阵的低秩分解性质.针对此问题,提出了基于多维度上下文的张量低秩分解的自学习方法,此方法基于张量的平行因子分解性质,加快了算法的收敛速度,降低了数据分析的复杂度.仿真结果验证了算法在移动用户偏好估计精度方面的有效性.