# A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model

arXiv: https://arxiv.org/abs/2208.02513 · HTML: https://mlsanity.com/paper/2208.02513/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 52 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Built on by (most cited first, 5 of 5)

- [Tensor Graph Convolutional Network for Dynamic Graph Representation Learning](https://mlsanity.com/paper/2401.07065/) (2024)
- [An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis](https://mlsanity.com/paper/2401.07012/) (2024)
- [Fast Latent Factor Analysis via a Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm](https://mlsanity.com/paper/2303.03941/) (2023)
- [High-order Graph Neural Networks with Common Neighbor Awareness for Link Prediction](https://mlsanity.com/paper/2504.18758/) (2025)
- [A Proportional-Integral Controller-Incorporated SGD Algorithm for High Efficient Latent Factor Analysis](https://mlsanity.com/paper/2508.17609/) (2025)

## Abstract (arXiv)

High-dimensional and incomplete (HDI) data holds tremendous interactive information in various industrial applications. A latent factor (LF) model is remarkably effective in extracting valuable information from HDI data with stochastic gradient decent (SGD) algorithm. However, an SGD-based LFA model suffers from slow convergence since it only considers the current learning error. To address this critical issue, this paper proposes a Nonlinear PID-enhanced Adaptive Latent Factor (NPALF) model with two-fold ideas: 1) rebuilding the learning error via considering the past learning errors following the principle of a nonlinear PID controller; b) implementing all parameters adaptation effectively following the principle of a particle swarm optimization (PSO) algorithm. Experience results on four representative HDI datasets indicate that compared with five state-of-the-art LFA models, the NPALF model achieves better convergence rate and prediction accuracy for missing data of an HDI data.