# Memory-efficient Embedding for Recommendations

arXiv: https://arxiv.org/abs/2006.14827 · HTML: https://mlsanity.com/paper/2006.14827/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders](https://mlsanity.com/paper/2210.12316/) (2022)
- [Learnable Embedding Sizes for Recommender Systems](https://mlsanity.com/paper/2101.07577/) (2021)
- [Deep Learning for Click-Through Rate Estimation](https://mlsanity.com/paper/2104.10584/) (2021)
- [AutoLoss: Automated Loss Function Search in Recommendations](https://mlsanity.com/paper/2106.06713/) (2021)
- [The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification](https://mlsanity.com/paper/2105.02778/) (2021)
- [Memorize, Factorize, or be Na"ive: Learning Optimal Feature Interaction Methods for CTR Prediction](https://mlsanity.com/paper/2108.01265/) (2021)
- [Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer](https://mlsanity.com/paper/2108.11513/) (2021)
- [Human-inspired Perspectives: A Survey on AI Long-term Memory](https://mlsanity.com/paper/2411.00489/) (2024)
- [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)
- [OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction](https://mlsanity.com/paper/2408.08585/) (2024)
- [Centrality-aware Product Retrieval and Ranking](https://mlsanity.com/paper/2410.15930/) (2024)
- [CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems](https://mlsanity.com/paper/2506.19993/) (2025)
- [Proxy-based Item Representation for Attribute and Context-aware Recommendation](https://mlsanity.com/paper/2312.06145/) (2023)
- [Efficient Mixed Dimension Embeddings for Matrix Factorization](https://mlsanity.com/paper/2205.11248/) (2022)

## Abstract (arXiv)

Practical large-scale recommender systems usually contain thousands of feature fields from users, items, contextual information, and their interactions. Most of them empirically allocate a unified dimension to all feature fields, which is memory inefficient. Thus it is highly desired to assign different embedding dimensions to different feature fields according to their importance and predictability. Due to the large amounts of feature fields and the nuanced relationship between embedding dimensions with feature distributions and neural network architectures, manually allocating embedding dimensions in practical recommender systems can be very difficult. To this end, we propose an AutoML based framework (AutoDim) in this paper, which can automatically select dimensions for different feature fields in a data-driven fashion. Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework. We conduct extensive experiments on benchmark datasets to validate the effectiveness of the AutoDim framework.