Summary of Pslf: a Pid Controller-incorporated Second-order Latent Factor Analysis Model For Recommender System, by Jialiang Wang et al.
PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System
by Jialiang Wang, Yan Xia, Ye Yuan
First submitted to arxiv on: 31 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed PID controller-incorporated SLF (PSLF) model demonstrates superior performance in graph representation learning, particularly for high-dimensional and incomplete interaction data. By refining learning error estimation and acquiring second-order information insights through Hessian-vector products, the PSLF model outperforms four state-of-the-art latent factor models based on advanced optimizers regarding convergence rates and generalization performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The PSLF model is a new approach to graph representation learning that uses a PID controller to help it learn better. It does this by refining its estimates of how well it’s doing and using information about the shape of the loss landscape to make better decisions. This results in faster convergence rates and better performance on incomplete data. |
Keywords
» Artificial intelligence » Generalization » Representation learning
