# Binary Code based Hash Embedding for Web-scale Applications

arXiv: https://arxiv.org/abs/2109.02471 · HTML: https://mlsanity.com/paper/2109.02471/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 24 Aug 2021.

## Builds on (most cited first, 21 of 21)

- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [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)
- [AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations](https://mlsanity.com/paper/2002.11252/) (2020)
- [Learnable Embedding Sizes for Recommender Systems](https://mlsanity.com/paper/2101.07577/) (2021)
- [Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems](https://mlsanity.com/paper/1909.11810/) (2019)
- [Neural Input Search for Large Scale Recommendation Models](https://mlsanity.com/paper/1907.04471/) (2019)
- [Compressing Word Embeddings via Deep Compositional Code Learning](https://mlsanity.com/paper/1711.01068/) (2017)
- [Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems](https://mlsanity.com/paper/2007.14523/) (2020)
- [Tensorized Embedding Layers for Efficient Model Compression](https://mlsanity.com/paper/1901.10787/) (2019)
- [Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems](https://mlsanity.com/paper/2002.08530/) (2020)
- [Memory-efficient Embedding for Recommendations](https://mlsanity.com/paper/2006.14827/) (2020)
- [Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks](https://mlsanity.com/paper/1706.03993/) (2017)
- [Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations](https://mlsanity.com/paper/1806.09464/) (2018)
- [Binary Embedding: Fundamental Limits and Fast Algorithm](https://mlsanity.com/paper/1502.05746/) (2015)
- [Superbloom: Bloom filter meets Transformer](https://mlsanity.com/paper/2002.04723/) (2020)
- [Multi-Hot Compact Network Embedding](https://mlsanity.com/paper/1903.03213/) (2019)

## Built on by (most cited first, 11 of 11)

- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2203.16218/) (2022)
- [Experimental Analysis of Large-scale Learnable Vector Storage Compression](https://mlsanity.com/paper/2311.15578/) (2023)
- [Clustered Embedding Learning for Recommender Systems](https://mlsanity.com/paper/2302.01478/) (2023)
- [CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models](https://mlsanity.com/paper/2312.03256/) (2023)
- [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)
- [Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems](https://mlsanity.com/paper/2401.04408/) (2024)
- [Unified Low-rank Compression Framework for Click-through Rate Prediction](https://mlsanity.com/paper/2405.18146/) (2024)
- [WindVE: Collaborative CPU-NPU Vector Embedding](https://mlsanity.com/paper/2504.14941/) (2025)
- [DimGrow: Memory-Efficient Field-level Embedding Dimension Search](https://mlsanity.com/paper/2505.12683/) (2025)

## Abstract (arXiv)

Nowadays, deep learning models are widely adopted in web-scale applications such as recommender systems, and online advertising. In these applications, embedding learning of categorical features is crucial to the success of deep learning models. In these models, a standard method is that each categorical feature value is assigned a unique embedding vector which can be learned and optimized. Although this method can well capture the characteristics of the categorical features and promise good performance, it can incur a huge memory cost to store the embedding table, especially for those web-scale applications. Such a huge memory cost significantly holds back the effectiveness and usability of EDRMs. In this paper, we propose a binary code based hash embedding method which allows the size of the embedding table to be reduced in arbitrary scale without compromising too much performance. Experimental evaluation results show that one can still achieve 99% performance even if the embedding table size is reduced 1000× smaller than the original one with our proposed method.