mlsanityData to 7 Oct 2026

Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems

arXiv 2007.14523 ↗First posted 28 Jul 2020cs.SI cs.LG stat.MLcited by 33

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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.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jul 2020.

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Citing papers per year: 2021: 6; 2022: 9; 2023: 6; 2024: 3; 2025: 6; 2026: 3.

33 papers.

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  1. 2026Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Modelscited by 78
  2. 2023Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendationscited by 50
  3. 2021Learnable Embedding Sizes for Recommender Systemscited by 42
  4. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  5. 2022RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendationcited by 28
  6. 2023Embedding in Recommender Systems: A Surveycited by 19
  7. 2025Enhancing Embedding Representation Stability in Recommendation Systems with Semantic IDcited by 19
  8. 2021Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameterscited by 17
  9. 2022AutoShard: Automated Embedding Table Sharding for Recommender Systemscited by 17
  10. 2022DreamShard: Generalizable Embedding Table Placement for Recommender Systemscited by 14
  11. 2022Learning Compressed Embeddings for On-Device Inferencecited by 13
  12. 2021Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layercited by 12
21 more
  1. 2023Experimental Analysis of Large-scale Learnable Vector Storage Compressioncited by 12
  2. 2021Binary Code based Hash Embedding for Web-scale Applicationscited by 11
  3. 2022Adaptive Low-Precision Training for Embeddings in Click-Through Rate Predictioncited by 11
  4. 2021Online Learning for Recommendations at Grubhubcited by 7
  5. 2023CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Modelscited by 7
  6. 2024Deep Feature Embedding for Tabular Datacited by 7
  7. 2022Learning to Collide: Recommendation System Model Compression with Learned Hash Functionscited by 5
  8. 2023Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Modelscited by 5
  9. 2021Position-based Hash Embeddings For Scaling Graph Neural Networkscited by 3
  10. 2025The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversioncited by 3
  11. 2025CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systemscited by 3
  12. 2022Efficient Mixed Dimension Embeddings for Matrix Factorizationcited by 1
  13. 2022Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systemscited by 1
  14. 2024Fine-Grained Embedding Dimension Optimization During Training for Recommender Systemscited by 1
  15. 2024An Enhanced Batch Query Architecture in Real-time Recommendationcited by 1
  16. 2023Extracting Text Representations for Terms and Phrases in Technical Domainscited by 0
  17. 2025Coarse-to-Fine Lightweight Meta-Embedding for ID-Based Recommendationcited by 0
  18. 2025ShuffleGate: Scalable Feature Optimization for Recommender Systems via Batch-wise Sensitivity Learningcited by 0
  19. 2025DimGrow: Memory-Efficient Field-level Embedding Dimension Searchcited by 0
  20. 2026Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenderscited by 0
  21. 2026Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systemscited by 0