Learning to Rank

Follow this topic Ask about this topic

Learning to rank studies how to order candidates—such as documents, products, or recommendations—so the most useful items appear first. Approaches include pointwise relevance prediction, pairwise methods such as RankNet and Bradley–Terry models, listwise objectives such as LambdaRank, and neural or LLM-based rerankers; reinforcement learning can also optimize rankings for downstream outcomes. Systems are evaluated with metrics such as NDCG and MRR on retrieval benchmarks and recommendation data, then often tested in online experiments. A central tension is that offline ranking gains may not translate to user or business value, while stronger reranking can add latency and remain vulnerable to bias, position effects, and unstable feedback.

Latest papers

273 papers27 papers in the last four weeks, down 25% on the four weeks before. 0.2% of all new papers.

Does Reasoning Make Search More Fair? Comparing Fairness in Reasoning and Non-Reasoning Rerankers
Mar 11, 2026Saron Samuel, Benjamin Van Durme, Eugene YangAlgorithmic Fairness LLM Reranking
Johns Hopkins University · Human Language Technology Center of Excellence
A Hybrid LTR-based System via Social Context Embedding for Recommending Solutions of Software Bugs in Developer Communities
Mar 7, 2026Fouzi Harrag, Mokdad KhemlicheRecommender Systems Software Engineering
Computer Sciences Department, College of Sciences, Ferhat Abbas University, Setif, Algeria
Learning to Rank the Initial Branching Order of SAT Solvers
Mar 7, 2026Arvid Eriksson, Gabriel Poesia, Roman Bresson +2Boolean Satisfiability Constraint Satisfaction
KTH Royal Institute of Technology · Kempner Institute at Harvard University · Mohamed Bin Zayed University of Artificial Intelligence
Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation
Mar 7, 2026Kai Cheng, Hao Wang, Wei Guo +4Online Knowledge Distillation Recommender Systems
University of Science and Technology of China, Hefei, China · Huawei, Shanghai, China · Shanghai Jiao Tong University, Shanghai, China +1
IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation
Mar 5, 2026Bosi Wen, Yilin Niu, Cunxiang Wang +5LLM-as-a-Judge Instruction Following
The Conversational Artificial Intelligence (CoAI) Group, Tsinghua University · Zhipu AI · University of Electronic Science and Technology of China
Generalizing Fair Top- $k$ Selection: An Integrative Approach
Mar 5, 2026Guangya CaiAlgorithmic Fairness Group Fairness
University of Minnesota, Twin Cities, MN, USA
On the Learnability of Offline Model-Based Optimization: A Ranking Perspective
Mar 4, 2026Shen-Huan Lyu, Rong-Xi Tan, Ke Xue +4Surrogate Modeling Distribution Shift
Key Laboratory of Water Big Data Technology of Ministry of Water Resources, College of Computer Science and Software Engineering, Hohai University, Nanjing, China · Department of Computer Science, City University of Hong Kong, Hong Kong, China · National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China +1
Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks
Mar 4, 2026Zhichao Yang, Jianjie Wang, Zhixianhe Zhang +4Image Quality Assessment Learning to Rank
School of Artificial Intelligence, Xidian University · State Key Laboratory of EMIM, Xidian University
Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems
Mar 4, 2026Pengfei Tong, Siyuan Chen, Chenwei Zhang +4Recommender Systems Learning to Rank
ByteDance
FlashEvaluator: Expanding Search Space with Parallel Sequence-Level Evaluation
Mar 3, 2026Chao Feng, Yuanhao Pu, Chenghao Zhang +5Efficient Inference Recommender Systems
Kuaishou Technology, Beijing, China
Active Bipartite Ranking with Smooth Posterior Distributions
Feb 27, 2026James Cheshire, Stephan ClémençonLearning to Rank Statistical Learning Theory
Telecom Paris
Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments
Feb 26, 2026Evangelia Christakopoulou, Vivekkumar Patel, Hemanth Velaga +3Learning to Rank
Apple, Cupertino, CA, USA · Apple, Seattle, WA, USA
From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review
Feb 25, 2026Uttamasha Anjally Oyshi, Susan GauchFairness in Recommender Systems Recommender Systems
Department of Electrical Engineering & Computer Science University of Arkansas Fayetteville, USA
Revisiting Text Ranking in Deep Research
Feb 25, 2026Chuan Meng, Litu Ou, Sean MacAvaney +1Deep Research Agents Learning to Rank
The University of Edinburgh Edinburgh, United Kingdom · University of Glasgow Glasgow, United Kingdom
When Alpha Breaks: Two-Level Uncertainty for Safe Deployment of Cross-Sectional Stock Rankers
Feb 24, 2026Ursina SanderinkQuantitative Finance Decision-Making under Uncertainty
PRECTR-V2:Unified Relevance-CTR Framework with Cross-User Preference Mining, Exposure Bias Correction, and LLM-Distilled Encoder Optimization
Feb 24, 2026Shuzhi Cao, Rong Chen, Ailong He +2CTR Prediction Learning to Rank
Alibaba Group, Hangzhou, China
ReAttn: Improving Attention-based Re-ranking via Attention Re-weighting
Feb 23, 2026Yuxing Tian, Fengran Mo, Weixu Zhang +2LLM Reranking Learning to Rank
University of Montreal · McGill University & MILA · International Digital Economy Academy
UniRank: A Multi-Agent Calibration Pipeline for Estimating University Rankings from Anonymized Bibliometric Signals
Feb 21, 2026Pedram Riyazimehr, Seyyed Ehsan MahmoudiHigher Education Language Model Calibration
NotionWave
CaliCausalRank: Calibrated Multi-Objective Ad Ranking with Robust Counterfactual Utility Optimization
Feb 21, 2026Xikai Yang, Sebastian Sun, Yilin Li +3Learning to Rank Multi-Objective Optimization
Columbia University, New York, USA · University of Wisconsin-Madison, Madison, USA · Carnegie Mellon University, Pittsburgh, USA +3
Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System
Feb 20, 2026Longfei Yun, Yihan Wu, Haoran Liu +13Learning to Rank
Meta, Menlo Park, United States
Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching
Feb 17, 2026Ren Kishimoto, Rikiya Takehi, Koichi Tanaka +4Recommender Systems Learning to Rank
Institute of Science Tokyo, Tokyo, Japan · Waseda University, Tokyo, Japan · Keio University, Tokyo, Japan +2
SuperLocalMemory: Privacy-Preserving Multi-Agent Memory with Bayesian Trust Defense Against Memory Poisoning
Feb 17, 2026Varun Pratap BhardwajAgent Memory Agent Memory Management
Independent Research
GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search
Feb 17, 2026Rong Fu, Jia Yee Tan, Chunlei Meng +4Information Retrieval Energy-Efficient ML
Independent Researcher
DenoiseRank: Learning to Rank by Diffusion Models
Feb 17, 2026Ying Wang, Preslav Nakov, Shangsong LiangLearning to Rank
Sun Yat-sen University, Guangzhou, China · MBZUAI, Abu Dhabi, UAE
DeepMTL2R: A Library for Deep Multi-task Learning to Rank
Feb 16, 2026Chaosheng Dong, Peiyao Xiao, Yijia Wang +1Learning to Rank Multi-Task Learning
Amazon USA
Aligning Language Model Benchmarks with Pairwise Preferences
Feb 2, 2026Marco Gutierrez, Xinyi Leng, Hannah Cyberey +3LLM Evaluation Learning to Rank
School of Data Science, University of Virginia · Imperial College London · Thomson Reuters Foundational Research +1
TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance
Oct 9, 2025Jianhui Yang, Yiming Jin, Pengkun Jiao +6RL for Language Models Reward Shaping
Tsinghua University Beijing, China · Taobao & Tmall Group of Alibaba Beijing, China · Taobao & Tmall Group of Alibaba Hangzhou, China +1
Ranking Before Serving: Low-Latency LLM Serving via Pairwise Learning-to-Rank
Sep 25, 2025Yiheng Tao, Yihe Zhang, Matthew Dearing +4LLM Serving Learning to Rank
University of Illinois Chicago, Chicago, IL, USA · Argonne National Laboratory, Lemont, IL, USA
Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings
Aug 16, 2025Jenny Y. Huang, Yunyi Shen, Dennis Wei +1Pairwise Preference Learning LLM Evaluation
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology · MIT-IBM Watson AI Lab · IBM Research
Algorithmic Shortlisting in Participatory Budgeting
Aug 7, 2025Juan Ignacio Zambrano, Clément Contet, Jairo Gudiño-Rosero +3Learning to Rank Privacy-Preserving ML
IRIT · Université Toulouse Capitole · Université de Toulouse +3

Mar 11, 2026J/K move · Enter open · S save

Does Reasoning Make Search More Fair? Comparing Fairness in Reasoning and Non-Reasoning Rerankers

Saron Samuel, Benjamin Van Durme, Eugene Yang

Johns Hopkins University · Human Language Technology Center of Excellence

Algorithmic Fairness LLM Reranking Learning to Rank

Open paper AskPDF arXiv

While reasoning rerankers, such as Rank1, have demonstrated strong abilities in improving ranking relevance, it is unclear how they perform on other retrieval qualities such as fairness. We conduct the first systematic comparison of fairness between reasoning and non-reasoning rerankers. Using the TREC 2022 Fair Ranking Track dataset, we evaluate six reranking models across multiple retrieval settings and demographic attributes. Our findings demonstrate reasoning neither improve nor harm fairness compared to non-reasoning approaches. Our fairness metric, Attention-Weighted Rank Fairness (AWRF) remained stable (0.33-0.35) across all models, even as relevance varies substantially (nDCG 0.247-1.000). Demographic breakdown analysis revealed fairness gaps for geographic attributes regardless of model architecture. These results indicate that future work in specializing reasoning models to be aware of fairness attributes could lead to improvements, as current implementations preserve the fairness characteristics of their input ranking.