# Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation

arXiv: https://arxiv.org/abs/1909.05475 · HTML: https://mlsanity.com/paper/1909.05475/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

## Built on by (most cited first, 19 of 19)

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- [Uncertainty Quantification and Decomposition for LLM-based Recommendation](https://mlsanity.com/paper/2501.17630/) (2025)
- [Dual Correction Strategy for Ranking Distillation in Top-N Recommender System](https://mlsanity.com/paper/2109.03459/) (2021)
- [Uncertainty Quantification for Fairness in Two-Stage Recommender Systems](https://mlsanity.com/paper/2205.15436/) (2022)
- [RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance](https://mlsanity.com/paper/2105.08820/) (2021)
- [Multi-domain Recommendation with Embedding Disentangling and Domain Alignment](https://mlsanity.com/paper/2308.05508/) (2023)
- [Unsupervised Multi-Index Semantic Hashing](https://mlsanity.com/paper/2103.14460/) (2021)
- [kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval](https://mlsanity.com/paper/2205.06205/) (2022)
- [Position-based Hash Embeddings For Scaling Graph Neural Networks](https://mlsanity.com/paper/2109.00101/) (2021)
- [CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems](https://mlsanity.com/paper/2506.19993/) (2025)
- [TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter](https://mlsanity.com/paper/2302.13915/) (2023)
- [Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems](https://mlsanity.com/paper/2311.06323/) (2023)
- [Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors](https://mlsanity.com/paper/2402.00053/) (2024)
- [Towards a Theoretical Understanding of Two Tower Recommendation Models](https://mlsanity.com/paper/2403.00802/) (2024)
- [Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders](https://mlsanity.com/paper/2604.07825/) (2026)
- [OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval](https://mlsanity.com/paper/2607.27475/) (2026)

## Abstract (arXiv)

Generating the Top-N recommendations from a large corpus is computationally expensive to perform at scale. Candidate generation and re-ranking based approaches are often adopted in industrial settings to alleviate efficiency problems. However it remains to be fully studied how well such schemes approximate complete rankings (or how many candidates are required to achieve a good approximation), or to develop systematic approaches to generate high-quality candidates efficiently. In this paper, we seek to investigate these questions via proposing a candidate generation and re-ranking based framework (CIGAR), which first learns a preference-preserving binary embedding for building a hash table to retrieve candidates, and then learns to re-rank the candidates using real-valued ranking models with a candidate-oriented objective. We perform a comprehensive study on several large-scale real-world datasets consisting of millions of users/items and hundreds of millions of interactions. Our results show that CIGAR significantly boosts the Top-N accuracy against state-of-the-art recommendation models, while reducing the query time by orders of magnitude. We hope that this work could draw more attention to the candidate generation problem in recommender systems.