8 citations · 10 across the 12 of their papers we have counts for
6 papers · 1 filter
Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction
Cheng Liang, Teng Huang, Yi He +3
User behavior data produced during interaction with massive items in the significant data era are generally heterogeneous and sparse, leaving the recommender system (RS) a large di…
PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis
Ye Yuan, Xin Luo
A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numero…
A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data
Di Wu, Peng Zhang, Yi He +1
High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of big data-related applications. Latent factor analysis (LFA) is a typical representation learning method…
Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices
Di Wu, Yi He, Xin Luo
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 hi…
High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection
Zhigang Liu, Xin Luo
Community describes the functional mechanism of a network, making community detection serve as a fundamental graph tool for various real applications like discovery of social circl…
Adaptive Divergence-based Non-negative Latent Factor Analysis
Ye Yuan, Guangxiao Yuan, Renfang Wang +1
High-Dimensional and Incomplete (HDI) data are frequently found in various industrial applications with complex interactions among numerous nodes, which are commonly non-negative f…