A Prediction-Sampling-Based Multilayer-Structured Latent Factor Model for Accurate Representation to High-Dimensional and Sparse Data

Document Type

Article

Publication Date

3-1-2024

Abstract

Performing highly accurate representation learning on a high-dimensional and sparse (HiDS) matrix is of great significance in a big data-related application such as a recommender system. A latent factor (LF) model is one of the most efficient approaches to the HiDS matrix representation. However, an LF model's representation learning ability relies heavily on an HiDS matrix's known data density, which is extremely low due to numerous missing data entities. To address this issue, this work proposes a prediction-sampling-based multilayer-structured LF (PMLF) model with twofold ideas: 1) constructing a loosely connected multilayered LF architecture to increase the known data density of an input HiDS matrix by generating synthetic data layer by layer and 2) constraining this synthetic data generating process through a random prediction-sampling strategy and nonlinear activations to avoid overfitting. In the experiments, PMLF is compared with six state-of-the-art LF-and deep neural network (DNN)-based models on four HiDS matrices from industrial applications. The results demonstrate that PMLF outperforms its peers in well-balancing prediction accuracy and computational efficiency.

Identifier

85137850071 (Scopus)

Publication Title

IEEE Transactions on Neural Networks and Learning Systems

External Full Text Location

https://doi.org/10.1109/TNNLS.2022.3200009

e-ISSN

21622388

ISSN

2162237X

PubMed ID

36083962

First Page

3845

Last Page

3858

Issue

3

Volume

35

Grant

61902370

Fund Ref

National Natural Science Foundation of China

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