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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Training with Missed Targets in Generative Recommendation: Separating Supervision from Probability Competition
Oct 7, 2026Xuesi Wang, Yangbin Shi, Xiaolin ZhengGenerative Recommendation Learning to Rank
Independent Researcher Shanghai, China · Zhejiang University Hangzhou, China
Global Average Precision for Representation Learning
Oct 7, 2026Bill Psomas, Mohammad Mahdi, Michalis Thomas +3Learning to Rank Metric Learning
VRG, FEE, Czech Technical University in Prague · INSAIT, Sofia University “St. Kliment Ohridski”
Learning a Ranking from Human Feedback in Log-Concave Random Utility Models
Oct 6, 2026Diego Alovisetti, Marco Mussi, Alberto Maria MetelliLearning to Rank
Politecnico di Milano
SIFT: Search Intent-to-Filter Transformer for Multi-Task Personalized Filter Ranking at Airbnb
Oct 6, 2026Shashank Dabriwal, Tanya Piplani, Hao Li +4Learning to Rank User Preference Modeling
Airbnb San Francisco, USA
MIRT: Transformers for Truthful Generative Auctions with Whole-feed Permutation Externalities
Oct 5, 2026Ali Elahi, Ermis Soumalias, Jason Cheuk Nam Liang +2Transformer Learning to Rank
University of Illinois Chicago · Meta
RankEvolve: A Reliable Multi-Agent Auto-Research Harness for Evolving Ranking Models
Sep 30, 2026Zheng Chen, Linfeng Liu, Hong Li +1Learning to Rank LLM Agent Orchestration
Meta
RankBuffer: Efficient Ranking-Based Rewards for Open-Ended Generation
Sep 29, 2026Zixuan Yang, Yiqun Chen, Qi Liu +6Open-Ended Generation Reward Modeling
Renmin University of China · University of Southern California · Xiaohongshu Inc.
Rethinking Personalized Generation: Test-Time Alignment via Factorized Ranking Models
Sep 28, 2026Qiyao Ma, Junshan Zhang, Zhe ZhaoLearning to Rank LLM Reranking
University of California, Davis
Hardware-Aware Features for CUTLASS Kernel Selection
Sep 28, 2026Shriram Chandran, Dominic Rinderer, Yakup Budanaz +3GPU Kernel Optimization Learning to Rank
ETH Zurich Zurich, Switzerland
Preserving DEG Rankings for Gene Discovery in Histology-Based Spatial Gene Expression Prediction
Sep 27, 2026Kaito Shiku, Kazuya Nishimura, Yasuhiro Kojima +1Spatial Transcriptomics Learning to Rank
Kyushu University, Japan · The University of Osaka, Japan · National Cancer Center, Japan
Multi-Dimensional Comparative Scale Construction for Efficient Personalized Subjective Judgment in High-Traffic Applications
Sep 27, 2026Xianglong Shi, Shifeng Liu, Sirui Zhao +2Human Preference Evaluation Learning to Rank
University of Science and Technology of China · University of Electronic Science and Technology of China
AdaTutoRank: Learning to Rerank Document Sets via Adaptive Tutoring Optimization for RAG and Deep Research
Sep 26, 2026Kailin Jiang, Lei Liu, Jian Xi +6Retrieval-Augmented Generation Learning to Rank
University of Science and Technology of China · Yuanbao Team, Tencent
Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores
Sep 24, 2026Sam Urmian, Qinyi Liu, Mohammad KhalilLearning to Rank Differential Privacy
University of Bergen Centre for the Science of Learning & Technology (SLATE) · City University of Macau School of Education
MORE-PLR: multi-output regression employed for partial label ranking
Sep 24, 2026Santo M. A. R. Thies, Juan C. Alfaro, Viktor BengsLearning to Rank
Chair of Artificial Intelligence and Machine Learning, Ludwig-Maximilians-Universität München, Munich, 80799, Germany. · Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain. · Laboratorio de Sistemas Inteligentes y Minería de Datos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain. +1
Lightweight Ranking Heads: Accelerating Multi-Task Experimentation in Production Recommender Systems
Sep 21, 2026Sanjay Surendranath Girija, Aniruddh Nath, Li Wei +6Learning to Rank Multi-Task Learning
Google LLC Mountain View, CA, USA
Beyond Relevance: Structured Semantic Supervision for Product Search with LLM-Augmented Annotations
Sep 20, 2026Girish A. Koushik, Swapnil Bhosale, Samarth Agrawal +4Learning to Rank LLM-Assisted Annotation
Computer Science, University of Surrey, Guildford, United Kingdom · People-Centred AI, University of Surrey, Guildford, United Kingdom · eBay USA +2
RLVR $^{2}$ : Reinforcement Learning with Verifiable Rubric-based Ranking
Sep 20, 2026Hao Li, Zhengkun Zhang, Gangqiang Hu +4Learning to Rank Rubric-Based RL
ERNIE Team, Baidu Inc.
Think Thrice Before Reranking: Multi-perspective Evidence and Reasoning Integration for Text Reranking
Sep 17, 2026Lijun Liu, Zhengzong Chen, Wenyan Li +2Learning to Rank RL for Language Model Reasoning
Honor Device Co., Ltd
LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era
Sep 16, 2026Venkat Srinivas, Chenzhang He, Sam Woodmansee +63Learning to Rank Recommender Systems
How Calibration Content Shapes Attention-Based Reranking
Sep 15, 2026Petros Karypis, Hossein Rajaby Faghihi, Peter Chen +4Post-Hoc Calibration Learning to Rank
UC San Diego · Google · Google DeepMind
Scaling Articulated Rationales for MLLM-based Recommendation
Sep 15, 2026Haoke Xiao, Yueyang Liu, Yuhui Zhang +17Learning to Rank Recommender Systems
Kuaishou Technology
Balancing Trial and Reorder: A Hybrid Sequential Transformer-GBDT Ranker for On-Demand Delivery
Sep 14, 2026Marcel Kurovski, Attila Nagy, Steffen Klempau +1Sequential Recommendation Learning to Rank
Wolt (DoorDash, Inc.) · Munich, Germany · Berlin, Germany +1
Accelerating Transfer-Learning-Based Autotuning with Predictive LLVM IR Performance Ranking
Sep 14, 2026Md Arafat Hossain, Thomas Randall, Akash Dutta +3Learning to Rank High-Performance Computing
School of Computing Clemson University Clemson, South Carolina, USA · MCS Argonne National Laboratory Lemont, Illinois, USA
PinDCO: Whole-Page Aware Dynamic Creative Optimization at Scale
Sep 14, 2026Yu Hao, Yuchun Li, Peimeng Sui +5Learning to Rank Online Advertising
Pinterest Inc.
RiPPLE: Cross-Space Performance Prediction from Early Training for Neural Architecture Search
Sep 14, 2026Yifan Yang, Zhaoyan Wang, Zheng Gao +2Learning to Rank Neural Architecture Search
University of New South Wales · Korea Advanced Institute of Science and Technology
OntologyAligner: Ontology-Aligned Retrieval and Hierarchy-Guided Large Language Model Reranking for Biomedical Ontology Normalization
Sep 9, 2026Jie Song, Zhichuan Xu, Ziyu Lu +8Learning to Rank LLM Reranking
Department of Dermatovenereology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610212, China · College of Health and Intelligent Engineering, Chengdu Medical College, Chengdu, Sichuan 610500, China
Agentic ML Exploration (A-MLE) for Ads Ranking
Sep 8, 2026Erwin Gao, Vinodh Kumar Sunkara, Jingyi Guan +36Learning to Rank LLM Agent Evaluation
Meta Platforms, Inc. Menlo Park and New York, USA
Comparing Retrieval Methods for Academic Advisor Discovery: A Six-Method Study of 768 CS Faculty Profiles Across 9 US Universities
Sep 3, 2026Biraj SubediLearning to Rank Hybrid Retrieval
Independent Researcher
On-Policy Distillation Meets Off-Policy GRPO: Training Compact Instruction-Following Rerankers
Sep 1, 2026Vignesh Prabhakar, Jialing Pan, Anil Babu AnkisettipalliReinforcement Learning Learning to Rank
SAP Labs, Palo Alto, CA, USA
VerTox: Verifiable Reward-Guided Corpus Poisoning Against Neural Ranking Models
Sep 1, 2026Zhiqi Huang, Vivek Datla, Zhichao Xu +3Learning to Rank RAG Poisoning Attacks
Capital One · University of Utah · Snowflake Inc.

Oct 7, 2026, cs.IRJ/K move · Enter open · S save

Training with Missed Targets in Generative Recommendation: Separating Supervision from Probability Competition

Xuesi Wang, Yangbin Shi, Xiaolin Zheng

Independent Researcher Shanghai, China · Zhejiang University Hangzhou, China

Generative Recommendation Learning to Rank

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Generative recommenders return a limited candidate set and may omit observed targets before reranking. A training strategy appends these missed targets to reranker training lists, although inference still ranks only original candidates. This operation simultaneously changes retrieved-target weight, adds supervision over appended targets, and makes the two groups compete for probability. An append/no-append comparison therefore cannot explain changes in returned-item rankings. We construct three matched losses that hold retrieved-target weight fixed while introducing appended-target supervision and group competition separately. The intermediate loss trains within both groups but normalizes them separately, preventing training-only targets from competing with inference candidates. Experiments with a released OneRec model and locally trained Amazon generators show that this competition can harm returned-item ranking. In four prespecified Amazon Video Games comparisons, removing it improved full-target normalized discounted cumulative gain (FT-NDCG) by 7.8--22.2%; 95% intervals over users and three of four intervals over training runs excluded zero. A conservative development-set rule selected appended-target training for two of three generators in one held-out category and rejected it for all three in another, avoiding a 1.7% loss. Candidate completion should therefore be evaluated for each generator rather than applied automatically.

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Latest papers 171