A High-dimensional and sparse (HiDS) matrix is frequently encountered in a
big data-related application like an e-commerce system or a social network
services system. To perform highly accurate representation learning on it is of
great significance owing to the great desire of extracting latent knowledge and
patterns from it. Latent factor analysis (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 perform such embeddings on a HiDS matrix directly
without exploiting its hidden graph structures, thereby resulting in accuracy
loss. To address this issue, this paper proposes a graph-incorporated latent
factor analysis (GLFA) model. It adopts two-fold ideas: 1) a graph is
constructed for identifying the hidden high-order interaction (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 representa-tion
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.

本文提出了一种基于图的潜因子分析模型，将隐藏的高阶交互与重复的低秩嵌入相结合，从而提高了对高维稀疏矩阵的表示学习能力，并在三个真实数据集上验证了其在预测丢失数据方面的优越性。