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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Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking
Jun 14, 2026Utshab Kumar Ghosh, Shubham ChatterjeeLearning to Rank Information Retrieval
Department of Computer Science Missouri University of Science and Technology Rolla, MO, USA
Surprise-Guided MergeSort: Budget-Efficient Human-in-the-Loop Ranking via Adaptive Comparison Scheduling
Jun 14, 2026Yujin Park, Haejun Chung, Ikbeom JangPairwise Preference Learning Pairwise Comparison
Hanyang University, Seoul, Republic of Korea · Hankuk University of Foreign Studies, Yongin, Republic of Korea
Mood-Aware Music Recommendation: Integrating User Affective Signals into Ranking Systems
Jun 11, 2026Terence Zeng, Abhishek K. UmrawalLearning to Rank Recommender Systems
University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
PRISMR: Overcoming Parse Collapse in Multimodal Listwise Ranking via Parameterized Representation Internalization
Jun 11, 2026Hao Jiang, Xin Li, Annan Wang +4Multimodal IR Learning to Rank
1Nanyang Technological University · 2Peking University · 3Independent Researcher
The Long Tail, Not the Front Page: Cold-Start Prediction of Crowd Highlight Salience
Jun 10, 2026Kazuki Nakayashiki, Keisuke WatanabeLearning to Rank
Glasp Inc.
ConvMemory v2: A Recall-Preserving Top-10 Evidence Reranker for Conversational Memory Retrieval
Jun 9, 2026Taiheng PanCross-Encoder Reranking Conversational Memory
School of Computing and Information Systems University of Melbourne
DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction
Jun 8, 2026Reazul Hasan Russel, Mingwei Tang, Rostam Shirani +18Learning to Rank User Modeling
AI at Meta
DeRA-MOS: Optimizing Text-to-Music Evaluation via Decoupled Listwise Ranking and Modality Alignment
Jun 8, 2026Chien-Chun Wang, Hung-Shin Lee, Hsin-Min Wang +1Text-to-Music Generation Music Generation Evaluation
E.SUN Financial Holding Co., Ltd., Taipei 10546, Taiwan · United Link Co., Ltd., Taipei 11493, Taiwan · Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan +1
Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
Jun 5, 2026Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov +8Algorithm Selection Pairwise Comparison
HSE University Moscow, Russian Federation
RETROSPECT: RETROsynthesis via Sequential Prediction, and Chemically Transformed-ranking
Jun 5, 2026Raja Sekhar Pappala, Shreyas Vinaya Sathyanarayana, Ronit Kumar Choudhary +2Transformer Learning to Rank
Mstack AI
Representation Curriculum: Stagewise Training for Robust Ranking and Allocation
Jun 3, 2026Ehsan Ebrahimzadeh, Sina Baharlouei, Abraham BagherjeiranCold-Start Recommendation Representation Learning
eBay Search Ranking and Monetization San Jose, California, USA
Trading Engagement for Sustainability: Carbon-Aware Re-ranking for E-commerce Recommendations
Jun 3, 2026Noah Lund Syrdal, Anders Vestrum, Jorgen BerghLearning to Rank Recommender Systems
University of California, Berkeley
Rethinking Sales Lead Scoring with LLM-based Hierarchical Preference Ranking
Jun 3, 2026Chenyu Zhang, Yiwen Liu, Yin Sun +4Learning to Rank LLM Reranking
Intelligent Business Team, Li Auto Inc.
DSIRM: Learning Query-Bridged Discrete Semantic Identifiers for E-commerce Relevance Modeling
Jun 3, 2026Bokang Wang, Xing Fang, Mingmin Jin +4Semantic ID Recommendation Learning to Rank
Taobao & Tmall Group of Alibaba Hangzhou, China
Structures Facilitate Retrieve, Rerank, and Generate
Jun 2, 2026Yeqin Zhang, Haomin Fu, Xujie Zhang +1Retrieval-Augmented Generation Learning to Rank
State Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China
When Does Complexity Conditioning Help a Frozen Sentence Embedding? A Controlled Study of Per-Sentence and Pair-Level Difficulty Adaptation
Jun 2, 2026Suhwan HwangSentence Embeddings Learning to Rank
Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysis
Jun 1, 2026Itai Zilberstein, Ioannis Anagnostides, Tuomas SandholmLearning to Rank Survival Analysis
Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA · Additional affiliations: Strategy Robot, Inc., Strategic Machine, Inc., Optimized Markets, Inc.
Semantic Retrieval for Product Search in E-Commerce
May 31, 2026Nikhil Kothari, Saksham Samdani, Ritam Mallick +3Learning to Rank Semantic Search
Flipkart, India
Test-Time Training for Zero-Resource Dense Retrieval Reranking
May 31, 2026Shiyan Liu, Yichen LiZero-Shot Learning Learning to Rank
Huazhong University of Science and Technology · ByteDance
UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale
May 29, 2026Hanyu Li, Yi-Ping Hsu, Aditya Mantha +17Sequential Recommendation Learning to Rank
Pinterest
Calibrated Preference Learning: The Case of Label Ranking
May 28, 2026Santo M. A. R. Thies, Viktor Bengs, Timo Kaufmann +2Probability Calibration Learning to Rank
Data Science and its Applications, Deutsches Forschungsinstitut für Künstliche Intelligenz (DFKI), Kaiserslautern, Germany · Munich Center for Machine Learning, Munich (MCML), Germany · Ludwig Maximilian Universität München, Munich, Germany
GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases
May 28, 2026Yicheng Tao, Yiqun Wang, Xiangchen Song +3Learning to Rank Graph-Based Retrieval
Department of Electrical Engineering and Computer Science, University of Michigan · Department of Computational Medicine and Bioinformatics, University of Michigan
MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality
May 28, 2026Yujin Park, Haejun Chung, Ikbeom JangLearning to Rank Active Learning
Hanyang University Seoul, Republic of Korea · Hankuk University of Foreign Studies Yongin, Republic of Korea
ConvMemory: A Lightweight Learned Memory Reranker, a Negative Attribution Result, and a Research-Preview Conflict Editor
May 27, 2026Taiheng PanCross-Encoder Reranking Learning to Rank
School of Computing and Information Systems University of Melbourne
RAGEAR: Retrieval-Augmented Graph-Enhanced Academic Recommender
May 26, 2026Francesco Granata, Lorenzo Lamazzi, Misael Mongiovì +2Learning to Rank Educational Technology
Department of Mathematics and Computer Science, University of Catania, Italy · Institute for Cognitive Science and Technology, National Research Council, Italy
Credit-assigned Policy Gradient for Early Stage Retrieval in Two-stage Ranking
May 25, 2026Haruka Kiyohara, Mihaela Curmei, Ariel Evnine +7Learning to Rank Information Retrieval
Computer Science Department, Cornell University, Ithaca, NY, USA · Central Applied Science, Meta, Menlo Park, CA, USA.
Neural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables
May 25, 2026Weizhi Fei, Hang Yin, Zihao Wang +3Learning to Rank Neuro-Symbolic Reasoning
Department of Mathematical Sciences, Tsinghua University Beijing, China · Squarepoint Capital Singapore, Singapore · Department of Computer Science and Engineering, Hong Kong University of Science and Technology Hong Kong, Hong Kong +1
DeGRe: Dense-supervised Generative Reranking for Recommendation
May 25, 2026Chaotian Song, Jingyao Zhang, Chenghao Chen +6Learning to Rank Generative Recommendation
College of Software, Zhejiang University Hangzhou, China · Rajax Network Technology, Taobao Shangou of Alibaba Hangzhou, China · Rajax Network Technology, Taobao Shangou of Alibaba Beijing, China +1
TempRet: Temporal Enhancement and Two-Stage Reranking for CVPR 2026 EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge
May 23, 2026Zixu Li, Yupeng Hu, Zhiwei Chen +4Learning to Rank Cross-Modal Retrieval
Shandong University · Harbin Institute of Technology (Shenzhen)
Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries
May 22, 2026Dongmin Lee, Anuran Makur, Japneet SinghLearning to Rank Pairwise Ranking
Department of Computer Science Purdue University West Lafayette, IN, USA · Department of Computer Science Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette, IN, USA

Jun 14, 2026, cs.IRJ/K move · Enter open · S save

Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking

Utshab Kumar Ghosh, Shubham Chatterjee

Department of Computer Science Missouri University of Science and Technology Rolla, MO, USA

Learning to Rank Information Retrieval IR Evaluation

Open paper AskPDF arXiv

Entity-aware document retrieval uses query-associated entities as ranking signals, assuming that semantically relevant entities are also useful retrieval signals. We show this assumption is insufficient- and explain why. Unlike terms, which are ground-truth observations, entity links are hypotheses produced by an imperfect linker: an entity can be topically central yet provide no discriminative signal if the linker fires indiscriminately across relevant and non-relevant documents. We formalize this as a distinction between Conceptual Entity Relevance (CER)- whether an entity is topically related to a query- and Observable Entity Relevance (OER)- whether its observed presence in a collection discriminates relevant from non-relevant documents. Across four collections and annotation sources including human entity judgments, CER and OER exhibit near-chance agreement (

κ\approx 0

), while OER operationalizations agree substantially (

κ\approx 0.5

), confirming CER as the systematic outlier. CER-based supervision selects topically plausible but weakly discriminative entities, pruning fewer than 4% of non-relevant documents on some collections. Aligning supervision with OER improves non-relevant pruning by up to 10x and open-world MAP by 0.051 over BM25. Our findings motivate a shift from conceptual to observable notions of entity relevance in entity-aware retrieval.

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Latest papers 171