A High-dimensional and s parse (HiDS) matrix is frequently encountered in Big Data-related applications such as e-commerce systems or wireless sensor networks. It is of great significance to perform highly accurate representation learning on an HiDS matrix due to the great desires of extracting latent knowledge from it. L atent f actor a nalysis (LFA), which represents an HiDS matrix by learning the low-rank embeddings based on its observed entries only, is one of the most effective and efficient approaches to this issue. However, most existing LFA-based models directly perform such embeddings on an HiDS matrix without exploiting its hidden graph structures, resulting in accuracy loss. To aid this issue, this paper proposes a g raph-incorporated l atent f actor a nalysis (GLFA) model. It adopts two-fold ideas: 1) a graph is constructed for identifying the hidden h igh- o rder i nteraction (HOI) among nodes described by an HiDS matrix, and 2) a recurrent LFA structure is carefully designed with the incorporation of HOI, thereby improving the representation learning ability of a resultant model. Experimental results on three real-world datasets demonstrate that GLFA outperforms six state-of-the-art models in predicting the missing data of an HiDS matrix, which evidently supports its strong representation learning ability to HiDS data.

中文翻译：

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高维稀疏数据的图结合潜在因子分析模型

一个高维和s 解析（HiDS）矩阵在大数据相关应用中经常遇到，例如电子商务系统或无线传感器网络。由于人们迫切希望从中提取潜在知识，因此在 HiDS 矩阵上进行高精度表示学习具有重要意义。潜伏因素分析（LFA）是解决这个问题最有效和高效的方法之一，它通过仅基于观察到的条目学习低秩嵌入来表示 HiDS 矩阵。然而，大多数现有的基于 LFA 的模型直接在 HiDS 矩阵上执行此类嵌入，而没有利用其隐藏的图结构，从而导致准确性损失。为了解决这个问题，本文提出了一个图形公司潜在的因素分析 (GLFA) 模型。它采用两重思想：1）构建一个图来识别隐藏的高的- 命令HiDS 矩阵描述的节点之间的交互（HOI），2）结合 HOI 精心设计了循环 LFA 结构，从而提高了所得模型的表示学习能力。在三个真实数据集上的实验结果表明，GLFA 在预测 HiDS 矩阵缺失数据方面优于六种最先进的模型，这显然支持了其对 HiDS 数据的强大表示学习能力。