OPS: An Order-Preserving Sorting Network for Information Retrieval
sigir26-072 · Full research paper · Chao Wang, Yongxiang Tang, Guikai Luan, Kaiyuan Li, Yanhua Cheng, Xialong Liu, Shu Lei Wu, Peng Jiang
Ranking Losses and Objectives · 100%
Abstract
Learning-to-rank (LTR) is a fundamental component of modern large-scale information retrieval (IR) systems, playing an essential role across various stages of the ranking pipeline. Recently, differentiable sorting networks have attracted increasing attention for LTR as a permutation-level learning paradigm, enabling end-to-end optimization directly on ranking structure. However, existing approaches suffer from two critical limitations: (i) permutation-matrix fidelity, i.e., the predicted soft permutation matrix may deviate from the exact hard permutation matrix required by permutation-level objectives; and (ii) uncertainty in target ordering arising from coarse or tied relevance labels, where the ground-truth order is set-valued rather than unique.
Assigned reviewers
| # | Reviewer | Match | Why (closest evidence publications in the taxonomy) | |
|---|---|---|---|---|
| 1 | Zhen Qin 0001 | 48 | 100 RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses (2023) Ranking Losses and Objectives 100 Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance (2023) Ranking Losses and Objectives 100 Scale Calibration of Deep Ranking Models (2022) Ranking Losses and Objectives |
|
| 2 | Rolf Jagerman | 47 | 100 On Optimizing Top-K Metrics for Neural Ranking Models (2022) Ranking Losses and Objectives 75 Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance (2023) Ranking Losses and Objectives 68 RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses (2023) Cross-Encoder and Neural Reranking |
Area chair
| Area chair | Match | |
|---|---|---|
| Fernando Diaz 0001 | 37 |
Area-chair candidates (4)
| Fernando Diaz 0001 assigned | 37 | 12 papers | |
| Jun Xu 0001 COI: co-author | 32 | 2 papers | |
| Xin Xin 0003 | 31 | 10 papers | |
| Xiaoxue Zang | 30 | 4 papers |
Candidates from the panel ranked by match score
| # | Reviewer | Match | Load | Why | |
|---|---|---|---|---|---|
| 1 | Sebastian Bruch 0001 | 50 | 3 | 100 A Stochastic Treatment of Learning to Rank Scoring Functions (2020) Ranking Losses and Objectives 100 Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks (2019) Ranking Losses and Objectives |
|
| 2 | Zhen Qin 0001 assigned | 48 | 3 | 100 RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses (2023) Ranking Losses and Objectives 100 Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance (2023) Ranking Losses and Objectives |
|
| 3 | Rolf Jagerman assigned | 47 | 3 | 100 On Optimizing Top-K Metrics for Neural Ranking Models (2022) Ranking Losses and Objectives 75 Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance (2023) Ranking Losses and Objectives |
|
| 4 | Yuchen Li 0006 | 43 | 3 | 51 MPGraf: a Modular and Pre-trained Graphformer for Learning to Rank at Web-scale (2023) Web-Scale Learning to Rank 51 RankElectra: Semi-supervised Pre-training of Learning-to-Rank Electra for Web-scale Search (2025) Web-Scale Learning to Rank |
|
| 5 | Xuanhui Wang | 42 | 3 | 75 Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance (2023) Ranking Losses and Objectives 73 Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning (2019) Unbiased Learning to Rank |
|
| 6 | Franco Maria Nardini | 41 | 3 | 100 Selective Gradient Boosting for Effective Learning to Rank (2018) Ranking Losses and Objectives 73 Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning (2019) Unbiased Learning to Rank |
|
| 7 | Yimeng Bai | 41 | 3 | 61 Unconstrained Monotonic Calibration of Predictions in Deep Ranking Systems (2025) CTR and Conversion Rate Prediction 39 GradCraft: Elevating Multi-task Recommendations through Holistic Gradient Crafting (2024) Multi-Task and Multi-Objective Optimization |
|
| 8 | Zechun Niu | 41 | 3 | 42 Distributionally Robust Optimization for Unbiased Learning to Rank (2025) Unbiased Learning to Rank 41 Addressing Personalized Bias for Unbiased Learning to Rank (2025) Unbiased Learning to Rank |
|
| 9 | Andrew Parry | 38 | 3 | 100 Towards a Relevance Posterior in Neural Information Access (2026) Ranking Losses and Objectives 38 Optimal Re-Ranking Depth (2026) Query Performance Prediction |
|
| 11 | Theresia Veronika Rampisela | 36 | 3 | 36 Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier (2025) Fairness Metrics and Evaluation 36 Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance (2024) Fairness Metrics and Evaluation |
|
| 12 | Shuaiqiang Wang | 35 | 3 | 51 GS2P: A Generative Pre-trained Learning to Rank Model with Over-parameterization for Web-Scale Search (Extended Abstract) (2024) Web-Scale Learning to Rank 51 GS2P: a generative pre-trained learning to rank model with over-parameterization for web-scale search (2024) Web-Scale Learning to Rank |
|
| 13 | Negar Arabzadeh | 34 | 3 | 40 Learning Context-aware Term Importance for Query Performance Prediction (2026) Query Performance Prediction 40 Query Performance Prediction Using Neural Query Space Proximity (2026) Query Performance Prediction |
|
| 14 | Keping Bi | 34 | 3 | 100 Learning a Deep Listwise Context Model for Ranking Refinement (2018) Ranking Losses and Objectives 51 Extracting search-focused key n-grams for relevance ranking in web search (2012) Web-Scale Learning to Rank |
|
| 15 | Zhengyi Yang 0007 | 34 | 3 | 40 On Softmax Direct Preference Optimization for Recommendation (2024) Preference Alignment and Optimization 40 Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization (2025) Preference Alignment and Optimization |
|
| 16 | Antonio Mallia | 34 | 3 | 34 Efficient In-Memory Inverted Indexes: Theory and Practice (2025) Efficient Retrieval and Indexing 34 A Comparison of Top-k Threshold Estimation Techniques for Disjunctive Query Processing (2020) Efficient Retrieval and Indexing |
|
| 17 | Tetsuya Sakai | 33 | 3 | 35 Flexible Pseudo-Relevance Feedback Using Optimization Tables (2001) Query Reformulation and Expansion 35 On the Ordering of Pooled Web Pages, Gold Assessments, and Bronze Assessments (2024) Test Collection Construction and Pooling |
|
| 18 | Jizhi Zhang | 33 | 3 | 40 TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation (2023) Preference Alignment and Optimization 40 Large Language Models are Learnable Planners for Long-Term Recommendation (2024) Preference Alignment and Optimization |
|
| 19 | Corby Rosset | 33 | 3 | 100 An Axiomatic Approach to Regularizing Neural Ranking Models (2019) Ranking Losses and Objectives 34 Optimizing Query Evaluations Using Reinforcement Learning for Web Search (2018) Efficient Retrieval and Indexing |
|
| 20 | Chongming Gao COI: co-author | 33 | 3 | 39 SPRec: Self-Play to Debias LLM-based Recommendation (2025) Preference Alignment and Optimization 39 Process-Supervised LLM Recommenders via Flow-guided Tuning (2025) Preference Alignment and Optimization |
|
| 21 | Shengyao Zhuang | 33 | 3 | 39 R2LLMs: Retrieval and Ranking with LLMs (2025) Reasoning-Intensive Retrieval and Ranking 37 Where Relevance Emerges: A Layer-Wise Study of Internal Attention for Zero-Shot Re-Ranking (2026) LLM-Based Reranking |
|
| 22 | Jurek Leonhardt | 33 | 3 | 42 Data Augmentation for Sample Efficient and Robust Document Ranking (2024) Ranking Distillation and Adaptation 35 Extractive Explanations for Interpretable Text Ranking (2023) Cross-Encoder and Neural Reranking |
|
| 23 | Xiao Wang 0043 | 33 | 3 | 34 Neural Pseudo-Relevance Feedback Models for Sparse and Dense Retrieval (2022) Query Reformulation and Expansion 34 ColBERT-PRF: Semantic Pseudo-Relevance Feedback for Dense Passage and Document Retrieval (2023) Query Reformulation and Expansion |
|
| 24 | Yuta Saito | 32 | 3 | 36 Off-Policy Evaluation of Ranking Policies under Diverse User Behavior (2023) Recommendation with Search and Ranking 34 Evaluating the Robustness of Off-Policy Evaluation (2021) Recommendation Systems and Evaluation |
|
| 26 | Xueguang Ma | 32 | 3 | 43 Tevatron: An Efficient and Flexible Toolkit for Neural Retrieval (2023) Learned Sparse Retrieval Efficiency 36 R2LLMs: Retrieval and Ranking with LLMs (2025) Reasoning-Intensive Retrieval and Ranking |
|
| 27 | Yang Zhang 0072 | 32 | 3 | 40 Reason4Rec: Deliberative User Preference Alignment of Large Language Models for Recommendation (2026) Preference Alignment and Optimization 40 TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation (2023) Preference Alignment and Optimization |
|
| 28 | Daiting Shi | 32 | 3 | 48 PRADA: Pre-Train Ranking Models With Diverse Relevance Signals Mined From Search Logs (2025) Unbiased Learning to Rank 41 Pre-trained Language Model based Ranking in Baidu Search (2021) Learned Sparse Retrieval Efficiency |
|
| 29 | Gustavo Penha | 31 | 3 | 44 On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search (2021) Conversational Search and Retrieval 42 Curriculum Learning Strategies for IR (2020) Ranking Distillation and Adaptation |
|
| 30 | Yongqi Li 0001 | 31 | 3 | 65 Learning to Rank in Generative Retrieval (2024) Generative Retrieval 34 EARN: Efficient Inference Acceleration for LLM-based Generative Recommendation by Register Tokens (2025) Generative Retrieval and Ranking |
|
| 31 | Leif Azzopardi | 31 | 3 | 40 When is query performance prediction effective? (2009) Query Performance Prediction 40 Query Variation Performance Prediction for Systematic Reviews (2018) Query Performance Prediction |
|
| 32 | Xiao Zhang 0034 | 31 | 3 | 39 UniSAR: Modeling User Transition Behaviors between Search and Recommendation (2024) Recommendation with Search and Ranking 35 When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation (2023) Recommendation with Search and Ranking |