# PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System

arXiv: https://arxiv.org/abs/2409.00448 · HTML: https://mlsanity.com/paper/2409.00448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 105 references that are in the map.
- First posted on arXiv 31 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [A Mini-Block Fisher Method for Deep Neural Networks](https://mlsanity.com/paper/2202.04124/) (2022)
- [Learning from Sets of Items in Recommender Systems](https://mlsanity.com/paper/1904.12643/) (2019)

## Abstract (arXiv)

A second-order-based latent factor (SLF) analysis model demonstrates superior performance in graph representation learning, particularly for high-dimensional and incomplete (HDI) interaction data, by incorporating the curvature information of the loss landscape. However, its objective function is commonly bi-linear and non-convex, causing the SLF model to suffer from a low convergence rate. To address this issue, this paper proposes a PID controller-incorporated SLF (PSLF) model, leveraging two key strategies: a) refining learning error estimation by incorporating the PID controller principles, and b) acquiring second-order information insights through Hessian-vector products. Experimental results on multiple HDI datasets indicate that the proposed PSLF model outperforms four state-of-the-art latent factor models based on advanced optimizers regarding convergence rates and generalization performance.