LGJun 23, 2023
Review of compressed embedding layers and their applications for recommender systems
arXiv:2306.13724v3h-index: 2
Originality Synthesis-oriented
AI Analysis
This is an incremental review that addresses the problem of model size in recommender systems for practitioners.
The paper reviews trainable compressed embedding layers and discusses their applicability for compressing large neural recommender systems, reporting measured results from their implementation.
We review the literature on trainable, compressed embedding layers and discuss their applicability for compressing gigantic neural recommender systems. We also report the results we measured with our compressed embedding layers.