BriefGPT.xyz
Jan, 2021
可学习的推荐系统嵌入尺寸
Learnable Embedding Sizes for Recommender Systems
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Siyi Liu, Chen Gao, Yihong Chen, Depeng Jin, Yong Li
TL;DR
本篇论文提出一种名为PEP(即插入嵌入剪枝)的新方法,通过将冗余参数修剪掉提高了基本模型的性能,同时可以自适应地从数据进行修剪阈值的学习,从而自动获得混合维度的嵌入方案,有效地减少了嵌入参数。
Abstract
The
embedding-based representation
learning is commonly used in
deep learning
recommendation models to map the raw sparse features to dense vectors. The traditional embedding manner that assigns a uniform size to
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