# Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems

arXiv: https://arxiv.org/abs/2007.14523 · HTML: https://mlsanity.com/paper/2007.14523/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 30 of 33)

- [Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models](https://mlsanity.com/paper/2601.07372/) (2026)
- [Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations](https://mlsanity.com/paper/2306.08121/) (2023)
- [Learnable Embedding Sizes for Recommender Systems](https://mlsanity.com/paper/2101.07577/) (2021)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation](https://mlsanity.com/paper/2201.10095/) (2022)
- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID](https://mlsanity.com/paper/2504.02137/) (2025)
- [Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters](https://mlsanity.com/paper/2111.05897/) (2021)
- [AutoShard: Automated Embedding Table Sharding for Recommender Systems](https://mlsanity.com/paper/2208.06399/) (2022)
- [DreamShard: Generalizable Embedding Table Placement for Recommender Systems](https://mlsanity.com/paper/2210.02023/) (2022)
- [Learning Compressed Embeddings for On-Device Inference](https://mlsanity.com/paper/2203.10135/) (2022)
- [Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer](https://mlsanity.com/paper/2108.11513/) (2021)
- [Experimental Analysis of Large-scale Learnable Vector Storage Compression](https://mlsanity.com/paper/2311.15578/) (2023)
- [Binary Code based Hash Embedding for Web-scale Applications](https://mlsanity.com/paper/2109.02471/) (2021)
- [Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction](https://mlsanity.com/paper/2212.05735/) (2022)
- [Online Learning for Recommendations at Grubhub](https://mlsanity.com/paper/2107.07106/) (2021)
- [CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models](https://mlsanity.com/paper/2312.03256/) (2023)
- [Deep Feature Embedding for Tabular Data](https://mlsanity.com/paper/2408.17162/) (2024)
- [Learning to Collide: Recommendation System Model Compression with Learned Hash Functions](https://mlsanity.com/paper/2203.15837/) (2022)
- [Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models](https://mlsanity.com/paper/2305.01868/) (2023)
- [Position-based Hash Embeddings For Scaling Graph Neural Networks](https://mlsanity.com/paper/2109.00101/) (2021)
- [The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversion](https://mlsanity.com/paper/2505.05605/) (2025)
- [CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems](https://mlsanity.com/paper/2506.19993/) (2025)
- [Efficient Mixed Dimension Embeddings for Matrix Factorization](https://mlsanity.com/paper/2205.11248/) (2022)
- [Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems](https://mlsanity.com/paper/2212.06264/) (2022)
- [Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems](https://mlsanity.com/paper/2401.04408/) (2024)
- [An Enhanced Batch Query Architecture in Real-time Recommendation](https://mlsanity.com/paper/2409.00400/) (2024)
- [Extracting Text Representations for Terms and Phrases in Technical Domains](https://mlsanity.com/paper/2305.15867/) (2023)
- [Coarse-to-Fine Lightweight Meta-Embedding for ID-Based Recommendation](https://mlsanity.com/paper/2501.11870/) (2025)
- [ShuffleGate: Scalable Feature Optimization for Recommender Systems via Batch-wise Sensitivity Learning](https://mlsanity.com/paper/2503.09315/) (2025)

## Abstract (arXiv)

Deep Neural Networks (DNNs) with sparse input features have been widely used in recommender systems in industry. These models have large memory requirements and need a huge amount of training data. The large model size usually entails a cost, in the range of millions of dollars, for storage and communication with the inference services. In this paper, we propose a hybrid hashing method to combine frequency hashing and double hashing techniques for model size reduction, without compromising performance. We evaluate the proposed models on two product surfaces. In both cases, experiment results demonstrated that we can reduce the model size by around 90 % while keeping the performance on par with the original baselines.