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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From Language to Behavior: Scaling Sequence Transformers for Industrial Recommendation Ranking with Rec-Native Designs
Sep 1, 2026Jie Chen, Xiangqian Yu, Yanchao Lian +9Sequential Recommendation Learning to Rank
ByteDance
CliffRank: A Dual-Branch Framework for Activity-Cliff Ranking Prediction
Sep 1, 2026Kewei Li, Rongying Zhang, Peiyu Yang +4Learning to Rank Pairwise Ranking
College of Computer Science and Technology, Jilin University, Changchun, 130012, China. · Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China. · Greenwich High School, Greenwich, CT, 06830, United States of America. +3
Replacing Training with Memory: Listwise Selection for Text-to-SQL
Sep 1, 2026Yeonseok Jeong, Soyoung Yoon, Seongjun Lee +1Memory-Augmented Language Models Learning to Rank
IPAI, Seoul National University · KAIST
Beyond Polarization: The Generative Constraint of Chain-of-Thought in Pointwise Reranking
Aug 31, 2026Xiaoyang Chen, Jie Liu, Haijin Liang +5Learning to Rank CoT Evaluation
University of Chinese Academy of Sciences · Tencent · University of Chinese Academy of Sciences, 2Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences
E-SENS: Exclusion-Sensitive Penalization for Negative-Constraint Retrieval
Aug 31, 2026Yerang Kim, Jiyoon Myung, Joohyung HanLearning to Rank Dense Retrieval
Independent Researcher · Lomin
MoPLEx: Estimating Plackett-Luce Mixture Models for Multi-Objective Alignment
Aug 25, 2026Dongyue Li, Ziniu Zhang, Lu Wang +1Learning to Rank Discrete Choice Modeling
Northeastern University, Boston, MA · University of Michigan, Ann Arbor, MI
Which Histories Matter for Time Series Forecasting? Learning Predictive Relevance with Future Supervision
Aug 24, 2026Yong-Hoon Choi, Kwang-Hyun Park, Youngjin ChoLearning to Rank Time Series Forecasting
Division of Robotics, Kwangwoon University Seoul 01897, Republic of Korea
DonorRank: Donor Language Selection for Low-Resource Cross-Lingual Speech Recognition
Aug 11, 2026Akriti Dhasmana, Aarohi Srivastava, David ChiangLearning to Rank Low-Resource Speech Recognition
Computer Science and Engineering University of Notre Dame Notre Dame, IN, USA
DACRI: Decision-Aware Causal Intervention Ranking for Critical Supply Chains
Aug 11, 2026Shiqi Huang, Jiani He, Dingyan Shang +4Learning to Rank Supply Chain Management
Independent Researcher
When Do Anchor-Based Pointwise LLM Rerankers Help? Retriever Quality, Statistical Scope, and Anchor Design
Aug 11, 2026Utshab Kumar Ghosh, Shubham ChatterjeeLearning to Rank LLM Reranking
Missouri University of Science and Technology Rolla, MO, USA
Fairness in Link Prediction Beyond Demographic Parity: A Reproducibility Study
Aug 10, 2026Valentijn Oldenburg, Floris de Kam, Stef de Wildt +1Learning to Rank Link Prediction
University of Amsterdam
Listwise Cross-Encoder Fine-Tuning vs. Agentic Instruction Tuning for LLM Rerankers: A Systematic Study in Medical Procedure Reranking
Aug 10, 2026Matan Fainzilber, Shlomit PlavnerCross-Encoder Reranking Learning to Rank
Healthee
PreGress: Ranking-Native Pre-training and Prompting for Graph Node Ranking
Aug 10, 2026Lujie Ban, Jiasheng shi, Yingli Zhou +5Learning to Rank Prompt Tuning
School of Data Science, The Chinese University of Hong Kong, Shenzhen, China · GTS, Huawei Technologies Co., Ltd., China
Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation
Aug 6, 2026Alfred M. Pastor, Maribel Castillo, Jose M. BadiaTensor Networks Learning to Rank
Department of Computer Science, Universitat de València, 46100 Burjassot, Spain · Department of Computer Science and Engineering, Universitat Jaume I, 12071 Castelló de la Plana, Spain
Multi-Objective Ranking for Live-Streaming: Balancing Fresh and Delayed Signals with Segment-Aware Targeting
Aug 5, 2026Xiaoyi Gu, Julia Tavares, Eder Santana +3Learning to Rank Recommender Systems
Twitch Interactive, San Francisco, California, USA · Amazon Prime Video, Sunnyvale, California, USA
Neighborhood-Aware Dual Biomedical Entity Linking
Aug 4, 2026Yicheng Tao, Jie LiuLearning to Rank Entity Linking
Department of Electrical Engineering and Computer Science, University of Michigan · Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan
Training Documents Reranker with Search Rubrics for Deep Research Agent
Aug 4, 2026Wenhan Liu, Yu Lu, Qiaolin Xia +8Deep Research Agents Learning to Rank
1Gaoling School of Artificial Intelligence, Renmin University of China · 2Tencent, Beijing, China
UniGD: A Unified Generative-Discriminative Framework for Industrial Retrieval
Aug 4, 2026Shujie Ji, Yawei Kong, Yilin Zhao +3Learning to Rank Online Advertising
Kuaishou Technology, Beijing, China
Aggregate-then-Calibrate for Human-centered Assessment with Theoretical Guarantees
Aug 3, 2026Zejun Xie, Xintong Li, Guang Wang +1Post-Hoc Calibration Learning to Rank
Rutgers University · Renmin University of China · Florida State University
TransNRank: Towards Accurate Neoantigen Ranking with Transformer
Aug 3, 2026Zhiyin An, Yuenan Hou, Shumeng Duan +3Learning to Rank Oncology
Retrieval Augmented Biomedical Question Answering with Weak Question Recovery and Neural Reranking for BioASQ Task 14b
Aug 2, 2026Xueying Zhao, Lee Mai, Balaji AnandganeshRetrieval-Augmented Generation Learning to Rank
Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332
On the Limits of Machine-Learned Ranking for Modern Microarchitectural Policies
Aug 2, 2026Yanxin Zhang, Shayne Wadle, Yuxuan Xiong +3Learning to Rank Electronic Design Automation
NVIDIA · University of Wisconsin–Madison
Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs
Jul 30, 2026Minwoo Yu, Young-guk HaLearning to Rank Dynamic Graph Learning
Smart Computing Laboratory, Department of Computer Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea
Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study
Jul 30, 2026Di Bai, Jintao Liu, Zhenwei Tang +3Learning to Rank Multi-Task Learning
Google LLC Mountain View, CA, USA · Google LLC Toronto, ON, Canada · Google LLC New York, NY, USA +1
Hierarchical Reranking for Scalable Financial RAG System
Jul 29, 2026Joohyun Lee, Sungwoo HongFinancial Services Retrieval-Augmented Generation
1Financial Security Institute · 2Hanyang University
OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval
Jul 29, 2026Ziwei Li, Shuyao Li, Xufeng Cai +12Learning to Rank Recommender Systems
Meta Platforms, Inc. ∗USA, †Singapore
Good Rankers, Bad Objectives: Bilinear Contrastive Critics under Expressive Policy Search
Jul 29, 2026Ayushman Singh, Siddharth AphaleLearning to Rank Contrastive RL
Stanford University
Structure-aware Relative Policy Optimization for Ranking
Jul 28, 2026Yiteng Tu, Weihang Su, Zitao Su +3Learning to Rank
Tsinghua University Beijing, China · Renmin University of China Beijing, China
TabRank: Chain-of-Thought Distillation for Table Re-Rankers
Jul 28, 2026Adarsh Singh, Kushal Raj Bhandari, Jianxi Gao +2Table QA CoT Distillation
1Arizona State University · 2Rensselaer Polytechnic Institute · 3Scale AI
MAPLE: Efficient and Diverse Multi-Alpha Generation for Portfolio Construction
Jul 27, 2026Yu-Chen Den, Kuan-Yu Chen, Kendro Vincent +1Learning to Rank Ensemble Learning
SinoPac Holdings · Taipei, Taiwan · National Chengchi University

Sep 1, 2026, cs.IRJ/K move · Enter open · S save

From Language to Behavior: Scaling Sequence Transformers for Industrial Recommendation Ranking with Rec-Native Designs

Jie Chen, Xiangqian Yu, Yanchao Lian, Tan Lu+8

ByteDance

Sequential Recommendation Learning to Rank Recommender Systems

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Scaling Transformers has driven large gains in language modeling, but transplanting this to behavior-sequence modeling in production ranking is challenging: recommendation differs in signal quality, where behavior sequences are noisy, temporally irregular, and sparsely supervised, and in computation asymmetry, where each request scores many candidates against one shared user history under tight latency budgets. We propose ReST, a recommendation-native Transformer scaling framework. For signal quality, it introduces a sequence encoder with dual-gated attention, rotary positional and temporal embedding, stabilized residual normalization, and training-only auxiliary objectives. For computation asymmetry, it factorizes ranking into a heavy reusable encoder and a lightweight cross decoder with projection-free KV attention and token-specific parameterization, coupling user-level shared-prefix training with shared-prefix serving for compute-once, decode-many-times ranking. Across industrial and public benchmarks, ReST achieves higher accuracy and scales more consistently along sequence length, depth, and width, where LLM-style Transformer blocks saturate. A one-week online A/B test on a production advertising platform improves online AUC by 1.31% and lifts a core revenue metric by 11.93% within a 50 ms P99 budget; ReST has since been fully deployed in production, showing that behavior-sequence scaling remains a promising, under-exploited axis for production ranking.

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Latest papers 171