# Embedding Compression in Recommender Systems: A Survey

arXiv: https://arxiv.org/abs/2408.02304 · HTML: https://mlsanity.com/paper/2408.02304/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2024.

## Built on by (most cited first, 14 of 14)

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- [Scalable Representation Learning for Multimodal Tabular Transactions](https://mlsanity.com/paper/2410.07851/) (2024)
- [Unveiling Discrete Clues: Superior Healthcare Predictions for Rare Diseases](https://mlsanity.com/paper/2501.16373/) (2025)
- [The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning](https://mlsanity.com/paper/2505.23176/) (2025)
- [The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems](https://mlsanity.com/paper/2505.11388/) (2025)
- [On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective](https://mlsanity.com/paper/2411.13052/) (2024)
- [Stochastic Communication Avoidance for Recommendation Systems](https://mlsanity.com/paper/2411.01611/) (2024)
- [DCN^2: Interplay of Implicit Collision Weights and Explicit Cross Layers for Large-Scale Recommendation](https://mlsanity.com/paper/2506.21624/) (2025)
- [Semantic Compression via Multimodal Representation Learning](https://mlsanity.com/paper/2509.24431/) (2025)
- [GRAB: An LLM-Inspired Sequence-First Click-Through Rate Prediction Modeling Paradigm](https://mlsanity.com/paper/2602.01865/) (2026)
- [Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems](https://mlsanity.com/paper/2604.18351/) (2026)
- [ADE: Adaptive Dictionary Embeddings -- Scaling Multi-Anchor Representations to Large Language Models](https://mlsanity.com/paper/2604.24940/) (2026)
- [ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation](https://mlsanity.com/paper/2607.10910/) (2026)
- [Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling](https://mlsanity.com/paper/2610.17963/) (2026)

## Abstract (arXiv)

To alleviate the problem of information explosion, recommender systems are widely deployed to provide personalized information filtering services. Usually, embedding tables are employed in recommender systems to transform high-dimensional sparse one-hot vectors into dense real-valued embeddings. However, the embedding tables are huge and account for most of the parameters in industrial-scale recommender systems. In order to reduce memory costs and improve efficiency, various approaches are proposed to compress the embedding tables. In this survey, we provide a comprehensive review of embedding compression approaches in recommender systems. We first introduce deep learning recommendation models and the basic concept of embedding compression in recommender systems. Subsequently, we systematically organize existing approaches into three categories, namely low-precision, mixed-dimension, and weight-sharing, respectively. Lastly, we summarize the survey with some general suggestions and provide future prospects for this field.