Cover Image

Correcting for Position Bias in Learning to Rank: A Control Function Approach

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Islam, Md Aminul, Vasilaky, Kathryn, Zheleva, Elena
Format:	Preprint
Published:	2025
Subjects:	Information Retrieval Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering
by: Islam, Md Aminul, et al.
Published: (2026)

Post-hoc Popularity Bias Correction in GNN-based Collaborative Filtering
by: Islam, Md Aminul, et al.
Published: (2025)

Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendations
by: Islam, Md Aminul, et al.
Published: (2025)

Linear-PAL: A Lightweight Ranker for Mitigating Shortcut Learning in Personalized, High-Bias Tabular Ranking
by: Pawar, Vipul Dinesh
Published: (2025)

Reducing Popularity Influence by Addressing Position Bias
by: Dzhoha, Andrii, et al.
Published: (2024)

EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading
by: Rahmana, Md Mahfujur, et al.
Published: (2026)

Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations
by: Igebaria, Reda, et al.
Published: (2024)

Document Set Expansion with Positive-Unlabeled Learning: A Density Estimation-based Approach
by: Zhang, Haiyang, et al.
Published: (2024)

Dual Correction Strategy for Ranking Distillation in Top-N Recommender System
by: Lee, Youngjune, et al.
Published: (2021)

RewardRank: Optimizing True Learning-to-Rank Utility
by: Bhatt, Gaurav, et al.
Published: (2025)

Progressive Refinement of E-commerce Search Ranking Based on Short-Term Activities of the Buyer
by: Sheng, Taoran, et al.
Published: (2025)

A Survey on E-Commerce Learning to Rank
by: Kabir, Md. Ahsanul, et al.
Published: (2024)

Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation
by: Gomez, Camilo, et al.
Published: (2026)

Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank
by: Gupta, Shashank, et al.
Published: (2024)

RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank
by: Chowdhury, Tanya, et al.
Published: (2024)

Localization Boosting for Growth Markets: Mitigating Cross-Locale Behavioral Bias in Learning-to-Rank
by: Seran, Suryaa Veerabathiran, et al.
Published: (2026)

Learning Cascade Ranking as One Network
by: Wang, Yunli, et al.
Published: (2025)

Controlling Gender Bias in Retrieval via a Backpack Architecture
by: Afzali, Amirabbas, et al.
Published: (2025)

Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees
by: Kang, Jingwei, et al.
Published: (2024)

Unlearning for Federated Online Learning to Rank: A Reproducibility Study
by: Tao, Yiling, et al.
Published: (2025)

A Design Space for Explainable Ranking and Ranking Models
by: Hazwani, I. Al, et al.
Published: (2022)

Learning to Rank with Variable Result Presentation Lengths
by: Knyazev, Norman, et al.
Published: (2025)

FullRecall: A Semantic Search-Based Ranking Approach for Maximizing Recall in Patent Retrieval
by: Ali, Amna, et al.
Published: (2025)

LTR-ICD: A Learning-to-Rank Approach for Automatic ICD Coding
by: Mansoori, Mohammad, et al.
Published: (2025)

Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation
by: Kannen, Nithish, et al.
Published: (2024)

Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank
by: Gupta, Shashank, et al.
Published: (2024)

DeepMTL2R: A Library for Deep Multi-task Learning to Rank
by: Dong, Chaosheng, et al.
Published: (2026)

Countering Mainstream Bias via End-to-End Adaptive Local Learning
by: Pan, Jinhao, et al.
Published: (2024)

Two-stage Risk Control with Application to Ranked Retrieval
by: Xu, Yunpeng, et al.
Published: (2024)

Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank
by: Hager, Philipp, et al.
Published: (2025)

RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation
by: Wu, Renzhi, et al.
Published: (2025)

Efficient and Effective Query Context-Aware Learning-to-Rank Model for Sequential Recommendation
by: Dzhoha, Andrii, et al.
Published: (2025)

A Self-boosted Framework for Calibrated Ranking
by: Zhang, Shunyu, et al.
Published: (2024)

Rank1: Test-Time Compute for Reranking in Information Retrieval
by: Weller, Orion, et al.
Published: (2025)

SPGL: Enhancing Session-based Recommendation with Single Positive Graph Learning
by: Liang, Tiantian, et al.
Published: (2024)

CaliCausalRank: Calibrated Multi-Objective Ad Ranking with Robust Counterfactual Utility Optimization
by: Yang, Xikai, et al.
Published: (2026)

Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction
by: Wu, Yi, et al.
Published: (2024)

Probing Ranking LLMs: A Mechanistic Analysis for Information Retrieval
by: Chowdhury, Tanya, et al.
Published: (2024)

Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation
by: Aouali, Imad, et al.
Published: (2022)

Scalable In-context Ranking with Generative Models
by: Gupta, Nilesh, et al.
Published: (2025)