Learning to Rank

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22 papers in the last four weeks, up 38% on the four weeks before. 0.2% of all new papers.

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Latest papers 171

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Learning to See Like Humans: Gaze-Aligned Cycling Safety Prediction
May 21, 2026Luís Maria Perdigão, Miguel Costa, Carlos Santiago +1Learning to Rank
Institute for Systems and Robotics (ISR-Lisboa), Instituto Superior Técnico, Lisbon, Portugal. · Technical University of Denmark, Kongens Lyngby, Denmark.
ECPO: Evidence-Coupled Policy Optimization for Evidence-Certified Candidate Ranking
May 21, 2026Miaobo Hu, Shuhao Hu, BoKun Wang +5Data Provenance Learning to Rank
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
PEARL: Unbiased Percentile Estimation via Contrastive Learning for Industrial-Scale Livestream Recommendation
May 20, 2026Blake Gella, Wei Wu, Yuhao Yin +6Contrastive Learning Learning to Rank
TikTok · San Jose, California, USA · ByteDance
VersusQ: Pairwise Margin Reasoning for Generalizable Video Quality Assessment
May 20, 2026Shibei Meng, Binxin Yang, Yuan Liu +4Pairwise Comparison Learning to Rank
The Chinese University of Hong Kong · Beijing Normal University · WeChat Vision, Tencent Inc. +2
GRASP: Deterministic argument ranking in interaction graphs
May 18, 2026Diganta Misra, Antonio Orvieto, Rediet Abebe +1LLM-as-a-Judge Learning to Rank
1MPI-IS Tübingen · 2Tübingen AI Center · 3ELLIS Institute Tübingen +2
Robust Player-Conditional Champion Ranking for League of Legends: Style Similarity, Mastery Priors, and Archetype-Constrained Discovery
May 18, 2026Min Heo, Pranav Kadiyam, Prasun PanthiLearning to Rank User Modeling
Wabash College, Crawfordsville, IN 47933, USA · Arizona State University, Tempe, AZ 85281, USA
Policy-Grounded Dynamic Facet Suggestions for Job Search
May 15, 2026Dan Xu, Baofen Zheng, Qianqi Shen +11Learning to Rank Information Retrieval
LinkedIn Corporation · Mountain View, CA, USA
A Unified Perturbation Framework for Analyzing Leaderboard Stability and Manipulation
May 15, 2026Hosna Oyarhoseini, Jimmy Lin, Amir-Hossein KarimiLLM Evaluation Learning to Rank
University of Waterloo, Waterloo, Ontario, Canada.
Margin-Adaptive Confidence Ranking for Reliable LLM Judgement
May 14, 2026Gaojie Jin, Yong Tao, Lijia Yu +1LLM Evaluation LLM-as-a-Judge
Department of Computer Science, University of Exeter · Institute of AI for Industries, Chinese Academy of Sciences · Department of Mathematics and Computer Science, Eindhoven University of Technology
Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing
May 14, 2026Mohammadhossein Ghahramani, Yan Qiao, Mengchu ZhouLearning to Rank Edge ML
Birmingham City University, UK · Macao Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology, Macau, China · Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Fortress: A Case Study in Stabilizing Search Recommendations via Temporal Data Augmentation and Feature Pruning
May 14, 2026Milind Pandurang Jagre, Jia Huang, Dayvid V. R. Oliveira +6Learning to Rank Recommender Systems
Apple
Active Learners as Efficient PRP Rerankers
May 14, 2026Jeremías Figueiredo Paschmann, Juan Kaplan, Francisco Nattero +3Pairwise Preference Learning Pairwise Comparison
ELIAS Lab, Departamento de Ingeniería, Universidad de San Andrés
PyroAdapt: Adapting Wildfire Prediction under Spatial Heterogeneity and Temporal Shift
May 12, 2026Enyi Jiang, Wu SunLearning to Rank Domain Adaptation
Siebel School of Computing and Data Science, University of Illiois at Urbana-Champaign · Computer Science, Stanford University · Department of Global Ecology, Carnegie Institution for Science
Very Efficient Listwise Multimodal Reranking for Long Documents
May 12, 2026Yiqun Sun, Pengfei Wei, Lawrence B. HsiehMultimodal IR Efficient Multimodal Inference
Magellan Technology Research Institute (MTRI), Japan.
Localization Boosting for Growth Markets: Mitigating Cross-Locale Behavioral Bias in Learning-to-Rank
May 11, 2026Suryaa Veerabathiran Seran, Ashwin Naresh Kumar, Tracy Holloway King +1Learning to Rank
Adobe San Jose, CA, USA
ConFit v3: Improving Resume-Job Matching with LLM-based Re-Ranking
May 10, 2026Xiao Yu, Ruize Xu, Chengyuan Xue +6Reinforcement Learning Learning to Rank
Columbia University · New York, NY, USA · John Hopkins University +6
Bringing Multimodal Large Language Models to Infrared-Visible Image Fusion Quality Assessment
May 7, 2026Yuchen Guo, Junli Gong, Yao Lu +3Infrared-Visible Image Fusion Learning to Rank
Northwestern University · Northeastern University · University of Washington +2
DynT2I-Eval: A Dynamic Evaluation Framework for Text-to-Image Models
May 7, 2026Juntong Wang, Jiarui Wang, Huiyu Duan +3Learning to Rank Benchmark Design
Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China
Beyond Retrieval: A Multitask Benchmark and Model for Code Search
May 6, 2026Siqiao Xue, Zihan Liao, Jin Qin +4Learning to Rank Software Engineering Benchmarks
Ant Group · Alipay
CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation
May 6, 2026Zhipeng Song, Yizhi Zhou, Xiangyu Kong +6Retrieval-Augmented Generation Learning to Rank
School of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, China · School of Information Engineering, Dalian Ocean University, No. 2-52, Heishijiao Street, Shahekou District, Dalian, 116023, China · School of Information Engineering, Liaodong University, No.116 Linjiang Back Street, Zhenan District, Dandong, 118001, China +3
Benchmarking Single-Pose Docking, Consensus Rescoring, and Supervised ML on the LIT-PCBA Library: A Critical Evaluation of DiffDock, AutoDock-GPU, GNINA, and DiffDock-NMDN
May 3, 2026Youssef Abo-Dahab, Xiaoiang Xiang, Joanne Chun +1Learning to Rank Virtual Screening
Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California, San Francisco, San Francisco, CA, USA · UCSF–Stanford Center of Excellence in Regulatory Science and Innovation (CERSI), San Francisco, CA, USA
Led to Mislead: Adversarial Content Injection for Attacks on Neural Ranking Models
May 2, 2026Amin Bigdeli, Amir Khosrojerdi, Radin Hamidi Rad +3Learning to Rank Adversarial Attacks
University of Waterloo, Canada · University of Toronto, Canada · Mila – Quebec AI Institute, Canada +1
LambdaRankIC: Directly Optimizing Rank IC for Financial Prediction
May 1, 2026Yan Lin, Yihong Su, Yi YangQuantitative Finance Learning to Rank
University of Macau · Hong Kong University of Science and Technology
Mind the Gap: Structure-Aware Consistency in Preference Learning
Apr 30, 2026Mehryar Mohri, Yutao ZhongPairwise Preference Learning LLM Alignment
Google Research, New York, NY · Courant Institute of Mathematical Sciences, New York, NY
One Pass, Any Order: Position-Invariant Listwise Reranking for LLM-Based Recommendation
Apr 30, 2026Ethan Bito, Yongli Ren, Estrid HeLearning to Rank LLM Reranking
RMIT University, Melbourne, Australia
Hypencoder Revisited: Reproducibility and Analysis of Non-Linear Scoring for First-Stage Retrieval
Apr 29, 2026Arne Eichholtz, Yongkang Li, Jutte Vijverberg +2Learning to Rank Hypernetworks
University of Amsterdam Amsterdam, The Netherlands
R $^3$ -SQL: Ranking Reward and Resampling for Text-to-SQL
Apr 28, 2026Hojae Han, Yeonseok Jeong, Seung-won Hwang +2Learning to Rank Text-to-SQL
ETRI · 2Seoul National University · 3Snowflake AI Research
Learning to Route Queries to Heads for Attention-based Re-ranking with Large Language Models
Apr 27, 2026Yuxing Tian, Fengran Mo, Zhiqi Huang +2Attention Head Analysis Learning to Rank
Université de Montréal · Capital One · McGill University & MILA
Explanation Quality Assessment as Ranking with Listwise Rewards
Apr 27, 2026Thomas Bailleux, Tanmoy Mukherjee, Emmanuel Lonca +2Reward Modeling Learning to Rank
CRIL, Univ. Artois & CNRS, France · Institut Universitaire de France
GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval
Apr 26, 2026Minghan Li, Tianrui Lv, Chao Zhang +1Legal IR Learning to Rank
Soochow University, Suzhou, China

May 21, 2026, cs.CVJ/K move · Enter open · S save

Learning to See Like Humans: Gaze-Aligned Cycling Safety Prediction

Luís Maria Perdigão, Miguel Costa, Carlos Santiago, Manuel Marques

Institute for Systems and Robotics (ISR-Lisboa), Instituto Superior Técnico, Lisbon, Portugal. · Technical University of Denmark, Kongens Lyngby, Denmark.

Learning to Rank

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Cycling delivers significant public-health and environmental benefits, yet its uptake in cities is often limited by perceived safety. When street environments appear unsafe, individuals are less likely to cycle, making perception a key barrier to adoption. Recent work has shown that pairwise comparisons of street-view images provide a scalable way to learn subjective safety judgments. However, existing approaches do not explicitly model human visual attention, which plays a central role in how humans perceive safety. We propose an Eye-Tracking-Guided Perceived Cycling Safety framework (EG-PCS) that integrates gaze data into a pairwise learning pipeline based on vision transformers. By supervising the model's attention mechanism with eye-tracking signals, we encourage alignment between learned attention maps and human fixation patterns. Experiments show that gaze-guided models achieve similar ranking performance compared to state-of-the-art approaches while producing attention maps that more accurately reflect human visual attention behavior. Our results demonstrate that incorporating eye-tracking information enhances both predictive accuracy and interpretability in perception-based urban analytics.

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Latest papers 171