# Neural Re-ranking in Multi-stage Recommender Systems: A Review

arXiv: https://arxiv.org/abs/2202.06602 · HTML: https://mlsanity.com/paper/2202.06602/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2022.

## Built on by (most cited first, 20 of 20)

- [CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking](https://mlsanity.com/paper/2412.01007/) (2024)
- [Discrete Conditional Diffusion for Reranking in Recommendation](https://mlsanity.com/paper/2308.06982/) (2023)
- [GR2: Generative Reasoning Re-ranker](https://mlsanity.com/paper/2602.07774/) (2026)
- [A Bi-metric Framework for Fast Similarity Search](https://mlsanity.com/paper/2406.02891/) (2024)
- [Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation](https://mlsanity.com/paper/2603.07107/) (2026)
- [TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter](https://mlsanity.com/paper/2302.13915/) (2023)
- [The Problem Is the Problem: Towards Scalable Mathematical Discovery](https://mlsanity.com/paper/2608.16977/) (2026)
- [EasyECR: A Library for Easy Implementation and Evaluation of Event Coreference Resolution Models](https://mlsanity.com/paper/2406.14106/) (2024)
- [Towards Principled Learning for Re-ranking in Recommender Systems](https://mlsanity.com/paper/2504.04188/) (2025)
- [Optimizing Compound Retrieval Systems](https://mlsanity.com/paper/2504.12063/) (2025)
- [Reasoning While Recommending: Entropy-Guided Latent Reasoning in Generative Re-ranking Models](https://mlsanity.com/paper/2601.13533/) (2026)
- [Hybrid and Collaborative Passage Reranking](https://mlsanity.com/paper/2305.09313/) (2023)
- [Vector search with small radiuses](https://mlsanity.com/paper/2403.10746/) (2024)
- [How to Surprisingly Consider Recommendations? A Knowledge-Graph-based Approach Relying on Complex Network Metrics](https://mlsanity.com/paper/2405.08465/) (2024)
- [Assistive AI for Augmenting Human Decision-making](https://mlsanity.com/paper/2410.14353/) (2024)
- [RADAR: Recall Augmentation through Deferred Asynchronous Retrieval](https://mlsanity.com/paper/2506.07261/) (2025)
- [Online Two-Stage Submodular Maximization](https://mlsanity.com/paper/2510.19480/) (2025)
- [Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking](https://mlsanity.com/paper/2604.07420/) (2026)
- [Lychee-Reranker: Fast but Not Late Interaction for Compressed Document Reranking](https://mlsanity.com/paper/2606.22807/) (2026)
- [GR2 Technical Report](https://mlsanity.com/paper/2606.31984/) (2026)

## Abstract (arXiv)

As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely applied in industrial applications. This review aims at integrating re-ranking algorithms into a broader picture, and paving ways for more comprehensive solutions for future research. For this purpose, we first present a taxonomy of current methods on neural re-ranking. Then we give a description of these methods along with the historic development according to their objectives. The network structure, personalization, and complexity are also discussed and compared. Next, we provide benchmarks of the major neural re-ranking models and quantitatively analyze their re-ranking performance. Finally, the review concludes with a discussion on future prospects of this field. A list of papers discussed in this review, the benchmark datasets, our re-ranking library LibRerank, and detailed parameter settings are publicly available at https://github.com/LibRerank-Community/LibRerank.