# Learning a Deep Listwise Context Model for Ranking Refinement

arXiv: https://arxiv.org/abs/1804.05936 · HTML: https://mlsanity.com/paper/1804.05936/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 30 of 62)

- [A Survey on Retrieval-Augmented Text Generation for Large Language Models](https://mlsanity.com/paper/2404.10981/) (2024)
- [Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback](https://mlsanity.com/paper/2108.13454/) (2021)
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- [Non-autoregressive Generative Models for Reranking Recommendation](https://mlsanity.com/paper/2402.06871/) (2024)
- [Joint Models for Answer Verification in Question Answering Systems](https://mlsanity.com/paper/2107.04217/) (2021)
- [PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation](https://mlsanity.com/paper/2203.12267/) (2022)
- [Variation Control and Evaluation for Generative SlateRecommendations](https://mlsanity.com/paper/2102.13302/) (2021)
- [STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals](https://mlsanity.com/paper/2406.15313/) (2024)
- [RankFormer: Listwise Learning-to-Rank Using Listwide Labels](https://mlsanity.com/paper/2306.05808/) (2023)
- [Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://mlsanity.com/paper/2203.15542/) (2022)
- [PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce](https://mlsanity.com/paper/2302.03487/) (2023)
- [Drowning in Documents: Consequences of Scaling Reranker Inference](https://mlsanity.com/paper/2411.11767/) (2024)
- [Paragraph-based Transformer Pre-training for Multi-Sentence Inference](https://mlsanity.com/paper/2205.01228/) (2022)
- [Set-to-Sequence Methods in Machine Learning: a Review](https://mlsanity.com/paper/2103.09656/) (2021)
- [ULTRA: An Unbiased Learning To Rank Algorithm Toolbox](https://mlsanity.com/paper/2108.05073/) (2021)
- [Question-Answer Sentence Graph for Joint Modeling Answer Selection](https://mlsanity.com/paper/2203.03549/) (2022)
- [Learning To Rank Diversely At Airbnb](https://mlsanity.com/paper/2210.07774/) (2022)
- [Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention](https://mlsanity.com/paper/2105.10019/) (2021)
- [Deep Ranking Ensembles for Hyperparameter Optimization](https://mlsanity.com/paper/2303.15212/) (2023)
- [NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems](https://mlsanity.com/paper/2502.06097/) (2025)
- [Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization](https://mlsanity.com/paper/2601.12078/) (2026)
- [CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking](https://mlsanity.com/paper/2112.08766/) (2021)
- [Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking](https://mlsanity.com/paper/2210.10695/) (2022)
- [Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models](https://mlsanity.com/paper/2310.08039/) (2023)
- [Beyond Item Dissimilarities: Diversifying by Intent in Recommender Systems](https://mlsanity.com/paper/2405.12327/) (2024)
- [Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of Relevance](https://mlsanity.com/paper/2503.23239/) (2025)
- [GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction](https://mlsanity.com/paper/2510.25220/) (2025)
- [Imitate TheWorld: A Search Engine Simulation Platform](https://mlsanity.com/paper/2107.07693/) (2021)
- [ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding](https://mlsanity.com/paper/2208.03030/) (2022)

## Abstract (arXiv)

Learning to rank has been intensively studied and widely applied in information retrieval. Typically, a global ranking function is learned from a set of labeled data, which can achieve good performance on average but may be suboptimal for individual queries by ignoring the fact that relevant documents for different queries may have different distributions in the feature space. Inspired by the idea of pseudo relevance feedback where top ranked documents, which we refer as the textitlocal ranking context, can provide important information about the query's characteristics, we propose to use the inherent feature distributions of the top results to learn a Deep Listwise Context Model that helps us fine tune the initial ranked list. Specifically, we employ a recurrent neural network to sequentially encode the top results using their feature vectors, learn a local context model and use it to re-rank the top results. There are three merits with our model: (1) Our model can capture the local ranking context based on the complex interactions between top results using a deep neural network; (2) Our model can be built upon existing learning-to-rank methods by directly using their extracted feature vectors; (3) Our model is trained with an attention-based loss function, which is more effective and efficient than many existing listwise methods. Experimental results show that the proposed model can significantly improve the state-of-the-art learning to rank methods on benchmark retrieval corpora.