# Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations

arXiv: https://arxiv.org/abs/2007.07203 · HTML: https://mlsanity.com/paper/2007.07203/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2020.

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

- [Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation](https://mlsanity.com/paper/2202.10226/) (2022)
- [RoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2408.08933/) (2024)
- [Large Language Model as Universal Retriever in Industrial-Scale Recommender System](https://mlsanity.com/paper/2502.03041/) (2025)
- [Practice with Graph-based ANN Algorithms on Sparse Data: Chi-square Two-tower model, HNSW, Sign Cauchy Projections](https://mlsanity.com/paper/2306.07607/) (2023)
- [Learning to Search Effective Example Sequences for In-Context Learning](https://mlsanity.com/paper/2503.08030/) (2025)
- [Tree-based Text-Vision BERT for Video Search in Baidu Video Advertising](https://mlsanity.com/paper/2209.08759/) (2022)
- [CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing](https://mlsanity.com/paper/2210.04288/) (2022)
- [pEBR: A Probabilistic Approach to Embedding Based Retrieval](https://mlsanity.com/paper/2410.19349/) (2024)
- [Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising](https://mlsanity.com/paper/2508.05206/) (2025)
- [An Efficient Embedding Based Ad Retrieval with GPU-Powered Feature Interaction](https://mlsanity.com/paper/2511.22460/) (2025)
- [Rethinking ANN-based Retrieval: Multifaceted Learnable Index for Large-scale Recommendation System](https://mlsanity.com/paper/2602.16124/) (2026)

## Abstract (arXiv)

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model, and then use some approximate nearest neighbor (ANN) search algorithm to find top candidates. In this paper, we present Deep Retrieval (DR), to learn a retrievable structure directly with user-item interaction data (e.g. clicks) without resorting to the Euclidean space assumption in ANN algorithms. DR's structure encodes all candidate items into a discrete latent space. Those latent codes for the candidates are model parameters and learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the structure is performed to retrieve the top candidates for reranking. Empirically, we first demonstrate that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline on two public datasets. Moreover, we show that, in a live production recommendation system, a deployed DR approach significantly outperforms a well-tuned ANN baseline in terms of engagement metrics. To the best of our knowledge, DR is among the first non-ANN algorithms successfully deployed at the scale of hundreds of millions of items for industrial recommendation systems.