Review of compressed embedding layers and their applications for recommender systems
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 23 Jun 2023.
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14 of 19 references.
References · 14
- 2019Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systemscited by 68
- 2021TT-Rec: Tensor Train Compression for Deep Learning Recommendation Modelscited by 58
- 2018Wide Compression: Tensor Ring Netscited by 50
- 2021HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Frameworkcited by 28
- 2022RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendationcited by 28
- 2019Tensorized Embedding Layers for Efficient Model Compressioncited by 27
- 2019Post-Training 4-bit Quantization on Embedding Tablescited by 22
- 2021Accelerating Recommendation System Training by Leveraging Popular Choicescited by 22
- 2022Learning Compressed Embeddings for On-Device Inferencecited by 13
- 2020Embedding Compression with Isotropic Iterative Quantizationcited by 10
- 2022Building a Performance Model for Deep Learning Recommendation Model Training on GPUscited by 10
- 2019Saec: Similarity-Aware Embedding Compression in Recommendation Systemscited by 7
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