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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.
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@article{hajgato2023review,
title = {Review of compressed embedding layers and their applications for recommender systems},
author = {Tamas Hajgato},
year = {2023},
eprint = {2306.13724},
archivePrefix = {arXiv},
url = {http://arxiv.org/abs/2306.13724v3}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/2306.13724)