# Towards Principled Learning for Re-ranking in Recommender Systems

arXiv: https://arxiv.org/abs/2504.04188 · HTML: https://mlsanity.com/paper/2504.04188/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 5 Apr 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [Learning a Deep Listwise Context Model for Ranking Refinement](https://mlsanity.com/paper/1804.05936/) (2018)
- [SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval](https://mlsanity.com/paper/1912.05891/) (2019)
- [Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time](https://mlsanity.com/paper/1711.07601/) (2017)
- [Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks](https://mlsanity.com/paper/1811.04415/) (2018)
- [Neural Re-ranking in Multi-stage Recommender Systems: A Review](https://mlsanity.com/paper/2202.06602/) (2022)
- [Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search](https://mlsanity.com/paper/1805.08524/) (2018)
- [PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation](https://mlsanity.com/paper/2203.12267/) (2022)
- [Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://mlsanity.com/paper/1902.00245/) (2019)
- [Edge-cloud Collaborative Learning with Federated and Centralized Features](https://mlsanity.com/paper/2304.05871/) (2023)
- [Attention over Self-attention:Intention-aware Re-ranking with Dynamic Transformer Encoders for Recommendation](https://mlsanity.com/paper/2201.05333/) (2022)
- [Prototypical Contrastive Learning and Adaptive Interest Selection for Candidate Generation in Recommendations](https://mlsanity.com/paper/2211.12893/) (2022)
- [Denoising Time Cycle Modeling for Recommendation](https://mlsanity.com/paper/2402.02718/) (2024)

## Built on by (most cited first, 1 of 1)

- [Break the Block: Dynamic-size Reasoning Blocks for Diffusion Large Language Models via Monotonic Entropy Descent with Reinforcement Learning](https://mlsanity.com/paper/2605.02263/) (2026)

## Abstract (arXiv)

As the final stage of recommender systems, re-ranking presents ordered item lists to users that best match their interests. It plays such a critical role and has become a trending research topic with much attention from both academia and industry. Recent advances of re-ranking are focused on attentive listwise modeling of interactions and mutual influences among items to be re-ranked. However, principles to guide the learning process of a re-ranker, and to measure the quality of the output of the re-ranker, have been always missing. In this paper, we study such principles to learn a good re-ranker. Two principles are proposed, including convergence consistency and adversarial consistency. These two principles can be applied in the learning of a generic re-ranker and improve its performance. We validate such a finding by various baseline methods over different datasets.