PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System
<|reference_start|>PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System: 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.<|reference_end|>
@article{wang2024pslf:,
    title={PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis
  Model for Recommender System},
    author={Jialiang Wang, Yan Xia, Ye Yuan},
    journal={arXiv preprint arXiv:2409.00448},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00448},
    primaryClass={cs.LG cs.AI cs.IR}
}