关键词: 大数据应用, 高维稀疏矩阵, 隐语义

Abstract: LF model is usually built by SGD method and it’s used to predict the missing data of high-dimensional sparse matrix in big data field. LF model need to integrate regularization terms to improve its performance. Due to [L1] regularization term is non-differentiable, normally integrates [L2] regularization term into an LF model only. However, the [L1] regularization normal can improve the sparsity and solving ability of LF model. To solve the issue, this paper proposes a SPGD_LF model that simultaneously integrates both [L1] and [L2] regularization terms in to an LF model. Since the objective function satisfies the Lipschitz condition and approximates the objective function by second-order Taylor expansion, a solver for stochastic gradient descent is constructed. In the process of stochastic gradient descent, the soft threshold process deals with the boundary optimization problem corresponding to the [L1] regularization term and solves the implicit semantic model. Through this optimization scheme, the characteristics of the known data in the target matrix in the latent factor space and the corresponding community relationship can be better expressed, and the generalization ability of the model is improved. Empirical studies on two datasets from industrial applications and the results show that the prediction accuracy, sparsity and convergence rate of SPGD_LF model are improved significantly.

Key words: big data application, high-dimensional and sparse matrix, latent factor