# Handling Large-scale Cardinality in building recommendation systems

arXiv: https://arxiv.org/abs/2401.09572 · HTML: https://mlsanity.com/paper/2401.09572/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://mlsanity.com/paper/1906.00091/) (2019)
- [Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems](https://mlsanity.com/paper/1909.02107/) (2019)
- [Hash Embeddings for Efficient Word Representations](https://mlsanity.com/paper/1709.03933/) (2017)
- [Cross-Batch Negative Sampling for Training Two-Tower Recommenders](https://mlsanity.com/paper/2110.15154/) (2021)

## Built on by (most cited first, 2 of 2)

- [Mamba for Scalable and Efficient Personalized Recommendations](https://mlsanity.com/paper/2409.17165/) (2024)
- [Using LLMs to Capture Users' Temporal Context for Recommendation](https://mlsanity.com/paper/2508.08512/) (2025)

## Abstract (arXiv)

Effective recommendation systems rely on capturing user preferences, often requiring incorporating numerous features such as universally unique identifiers (UUIDs) of entities. However, the exceptionally high cardinality of UUIDs poses a significant challenge in terms of model degradation and increased model size due to sparsity. This paper presents two innovative techniques to address the challenge of high cardinality in recommendation systems. Specifically, we propose a bag-of-words approach, combined with layer sharing, to substantially decrease the model size while improving performance. Our techniques were evaluated through offline and online experiments on Uber use cases, resulting in promising results demonstrating our approach's effectiveness in optimizing recommendation systems and enhancing their overall performance.