# GRN: Generative Rerank Network for Context-wise Recommendation

arXiv: https://arxiv.org/abs/2104.00860 · HTML: https://mlsanity.com/paper/2104.00860/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2021.

## Built on by (most cited first, 13 of 13)

- [Cross DQN: Cross Deep Q Network for Ads Allocation in Feed](https://mlsanity.com/paper/2109.04353/) (2021)
- [Discrete Conditional Diffusion for Reranking in Recommendation](https://mlsanity.com/paper/2308.06982/) (2023)
- [Non-autoregressive Generative Models for Reranking Recommendation](https://mlsanity.com/paper/2402.06871/) (2024)
- [PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce](https://mlsanity.com/paper/2302.03487/) (2023)
- [NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems](https://mlsanity.com/paper/2502.06097/) (2025)
- [GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction](https://mlsanity.com/paper/2510.25220/) (2025)
- [Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation](https://mlsanity.com/paper/2603.07107/) (2026)
- [Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks in E-Commerce Search](https://mlsanity.com/paper/2408.05751/) (2024)
- [Mobile Supply: The Last Piece of Jigsaw of Recommender System](https://mlsanity.com/paper/2308.03855/) (2023)
- [From Features to Transformers: Redefining Ranking for Scalable Impact](https://mlsanity.com/paper/2502.03417/) (2025)
- [FlashEvaluator: Expanding Search Space with Parallel Sequence-Level Evaluation](https://mlsanity.com/paper/2603.02565/) (2026)
- [Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking](https://mlsanity.com/paper/2604.07420/) (2026)
- [LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era](https://mlsanity.com/paper/2609.18148/) (2026)

## Abstract (arXiv)

Reranking is attracting incremental attention in the recommender systems, which rearranges the input ranking list into the final rank-ing list to better meet user demands. Most existing methods greedily rerank candidates through the rating scores from point-wise or list-wise models. Despite effectiveness, neglecting the mutual influence between each item and its contexts in the final ranking list often makes the greedy strategy based reranking methods sub-optimal. In this work, we propose a new context-wise reranking framework named Generative Rerank Network (GRN). Specifically, we first design the evaluator, which applies Bi-LSTM and self-attention mechanism to model the contextual information in the labeled final ranking list and predict the interaction probability of each item more precisely. Afterwards, we elaborate on the generator, equipped with GRU, attention mechanism and pointer network to select the item from the input ranking list step by step. Finally, we apply cross-entropy loss to train the evaluator and, subsequently, policy gradient to optimize the generator under the guidance of the evaluator. Empirical results show that GRN consistently and significantly outperforms state-of-the-art point-wise and list-wise methods. Moreover, GRN has achieved a performance improvement of 5.2% on PV and 6.1% on IPV metric after the successful deployment in one popular recommendation scenario of Taobao application.