# The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems

arXiv: https://arxiv.org/abs/2505.11388 · HTML: https://mlsanity.com/paper/2505.11388/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Sparse Autoencoders Find Highly Interpretable Features in Language Models](https://mlsanity.com/paper/2309.08600/) (2023)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Nomic Embed: Training a Reproducible Long Context Text Embedder](https://mlsanity.com/paper/2402.01613/) (2024)
- [k-Sparse Autoencoders](https://mlsanity.com/paper/1312.5663/) (2013)
- [Simple and Effective Dimensionality Reduction for Word Embeddings](https://mlsanity.com/paper/1708.03629/) (2017)
- [Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation](https://mlsanity.com/paper/2503.01776/) (2025)
- [Post-Training 4-bit Quantization on Embedding Tables](https://mlsanity.com/paper/1911.02079/) (2019)
- [Embedding Compression in Recommender Systems: A Survey](https://mlsanity.com/paper/2408.02304/) (2024)
- [Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction](https://mlsanity.com/paper/2212.05735/) (2022)
- [beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems](https://mlsanity.com/paper/2409.10309/) (2024)

## Built on by (most cited first, 4 of 4)

- [Learning Retrieval Models with Sparse Autoencoders](https://mlsanity.com/paper/2603.13277/) (2026)
- [Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Control](https://mlsanity.com/paper/2507.12202/) (2025)
- [Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems](https://mlsanity.com/paper/2604.18351/) (2026)
- [Tokens are All You Need: Dual-purpose Semantic IDs for Achieving LLM-Level I/O Efficiency in recommendation systems](https://mlsanity.com/paper/2607.24865/) (2026)

## Abstract (arXiv)

Industry-scale recommender systems face a core challenge: representing entities with high cardinality, such as users or items, using dense embeddings that must be accessible during both training and inference. However, as embedding sizes grow, memory constraints make storage and access increasingly difficult. We describe a lightweight, learnable embedding compression technique that projects dense embeddings into a high-dimensional, sparsely activated space. Designed for retrieval tasks, our method reduces memory requirements while preserving retrieval performance, enabling scalable deployment under strict resource constraints. Our results demonstrate that leveraging sparsity is a promising approach for improving the efficiency of large-scale recommenders. We release our code at https://github.com/recombee/CompresSAE.