Cover Image

Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hager, Philipp, Zoeter, Onno, de Rijke, Maarten
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

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

CLAX: Fast and Flexible Neural Click Models in JAX
by: Hager, Philipp, et al.
Published: (2025)

Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset
by: Hager, Philipp, et al.
Published: (2024)

Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models
by: de Haan, Morris, et al.
Published: (2024)

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

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

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

Towards Two-Stage Counterfactual Learning to Rank
by: Gupta, Shashank, et al.
Published: (2025)

Contrastive Learning for Diversity-Aware Product Recommendations in Retail
by: Karlis, Vasileios, et al.
Published: (2026)

Multi-granular Adversarial Attacks against Black-box Neural Ranking Models
by: Liu, Yu-An, et al.
Published: (2024)

An Offline Metric for the Debiasedness of Click Models
by: Deffayet, Romain, et al.
Published: (2023)

Optimal Baseline Corrections for Off-Policy Contextual Bandits
by: Gupta, Shashank, et al.
Published: (2024)

RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models
by: Heuss, Maria, et al.
Published: (2024)

ContextGNN: Beyond Two-Tower Recommendation Systems
by: Yuan, Yiwen, et al.
Published: (2024)

Ranked List Truncation for Large Language Model-based Re-Ranking
by: Meng, Chuan, et al.
Published: (2024)

Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank
by: Chen, Mouxiang, et al.
Published: (2023)

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

RankTower: A Synergistic Framework for Enhancing Two-Tower Pre-Ranking Model
by: Yan, YaChen, et al.
Published: (2024)

Large Language Models for Next Point-of-Interest Recommendation
by: Li, Peibo, et al.
Published: (2024)

Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank
by: Lutz, Yunus, et al.
Published: (2025)

Confounding is a Pervasive Problem in Real World Recommender Systems
by: Merkov, Alexander, et al.
Published: (2025)

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

Refine-POI: Reinforcement Fine-Tuned Large Language Models for Next Point-of-Interest Recommendation
by: Li, Peibo, et al.
Published: (2025)

The Impact of Group Membership Bias on the Quality and Fairness of Exposure in Ranking
by: Vardasbi, Ali, et al.
Published: (2023)

Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank
by: Loveland, Donald, et al.
Published: (2024)

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

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

Suggest, Complement, Inspire: Story of Two Tower Recommendations at Allegro.com
by: Osowska-Kurczab, Aleksandra, et al.
Published: (2025)

Separating and Learning Latent Confounders to Enhancing User Preferences Modeling
by: Xu, Hangtong, et al.
Published: (2023)

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

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

Where to Explore: A Reach and Cost-Aware Approach for Unbiased Data Collection in Recommender Systems
by: Chen, Qiang, et al.
Published: (2025)

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

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

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

Understanding Visual Saliency of Outlier Items in Product Search
by: Sarvi, Fatemeh, et al.
Published: (2025)

Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval
by: Mekonnen, Kidist Amde, et al.
Published: (2025)

MLPlatt: Simple Calibration Framework for Ranking Models
by: Bajger, Piotr, et al.
Published: (2026)

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

Theoretical Guarantees for LT-TTD: A Unified Transformer-based Architecture for Two-Level Ranking Systems
by: Abraich, Ayoub
Published: (2025)