# Saec: Similarity-Aware Embedding Compression in Recommendation Systems

arXiv: https://arxiv.org/abs/1903.00103 · HTML: https://mlsanity.com/paper/1903.00103/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [Accelerating Recommendation System Training by Leveraging Popular Choices](https://mlsanity.com/paper/2103.00686/) (2021)
- [Heterogeneous Acceleration Pipeline for Recommendation System Training](https://mlsanity.com/paper/2204.05436/) (2022)
- [CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems](https://mlsanity.com/paper/2506.19993/) (2025)
- [DQRM: Deep Quantized Recommendation Models](https://mlsanity.com/paper/2410.20046/) (2024)
- [Review of compressed embedding layers and their applications for recommender systems](https://mlsanity.com/paper/2306.13724/) (2023)
- [Accelerating Recommender Model Training by Dynamically Skipping Stale Embeddings](https://mlsanity.com/paper/2404.04270/) (2024)
- [Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems](https://mlsanity.com/paper/2604.18351/) (2026)

## Abstract (arXiv)

Production recommendation systems rely on embedding methods to represent various features. An impeding challenge in practice is that the large embedding matrix incurs substantial memory footprint in serving as the number of features grows over time. We propose a similarity-aware embedding matrix compression method called Saec to address this challenge. Saec clusters similar features within a field to reduce the embedding matrix size. Saec also adopts a fast clustering optimization based on feature frequency to drastically improve clustering time. We implement and evaluate Saec on Numerous, the production distributed machine learning system in Tencent, with 10-day worth of feature data from QQ mobile browser. Testbed experiments show that Saec reduces the number of embedding vectors by two orders of magnitude, compresses the embedding size by ~27x, and delivers the same AUC and log loss performance.