Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hager, Philipp, Deffayet, Romain, Renders, Jean-Michel, Zoeter, Onno, de Rijke, Maarten
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Information Retrieval Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

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

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

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

SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments
di: Deffayet, Romain, et al.
Pubblicazione: (2023)

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

Orcheo: A Modular Full-Stack Platform for Conversational Search
di: Jiang, Shaojie, et al.
Pubblicazione: (2026)

DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu Maps
di: Fan, Miao, et al.
Pubblicazione: (2024)

Whole Page Unbiased Learning to Rank
di: Mao, Haitao, et al.
Pubblicazione: (2022)

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

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

SubSearch: Intermediate Rewards for Unsupervised Guided Reasoning in Complex Retrieval
di: Petcu, Roxana, et al.
Pubblicazione: (2026)

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

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

Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank
di: Yu, Lulu, et al.
Pubblicazione: (2024)

AGENT-CQ: Automatic Generation and Evaluation of Clarifying Questions for Conversational Search with LLMs
di: Siro, Clemencia, et al.
Pubblicazione: (2024)

A Large-Scale Web Search Dataset for Federated Online Learning to Rank
di: Gregoriadis, Marcel, et al.
Pubblicazione: (2025)

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

Distributional Reinforcement Learning with Dual Expectile-Quantile Regression
di: Jullien, Sami, et al.
Pubblicazione: (2023)

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

Cognitive Biases in Large Language Models for News Recommendation
di: Lyu, Yougang, et al.
Pubblicazione: (2024)

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

MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search
di: Fan, Miao, et al.
Pubblicazione: (2024)

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

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

KuaiSearch: A Large-Scale E-Commerce Search Dataset for Recall, Ranking, and Relevance
di: Li, Yupeng, et al.
Pubblicazione: (2026)

Attack-in-the-Chain: Bootstrapping Large Language Models for Attacks Against Black-box Neural Ranking Models
di: Liu, Yu-An, et al.
Pubblicazione: (2024)

Generative Retrieval Meets Multi-Graded Relevance
di: Tang, Yubao, et al.
Pubblicazione: (2024)

Addressing Personalized Bias for Unbiased Learning to Rank
di: Niu, Zechun, et al.
Pubblicazione: (2025)

Enhancing and Scaling Search Query Datasets for Recommendation Systems
di: Rodrigues, Aaron, et al.
Pubblicazione: (2025)

OrLog: Resolving Complex Queries with LLMs and Probabilistic Reasoning
di: Hoveyda, Mohanna, et al.
Pubblicazione: (2026)

DeepShop: A Benchmark for Deep Research Shopping Agents
di: Lyu, Yougang, et al.
Pubblicazione: (2025)

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

InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization
di: Jin, Jiarui, et al.
Pubblicazione: (2024)

QBD-RankedDataGen: Generating Custom Ranked Datasets for Improving Query-By-Document Search Using LLM-Reranking with Reduced Human Effort
di: Gopalakrishnan, Sriram, et al.
Pubblicazione: (2025)

Document Similarity Enhanced IPS Estimation for Unbiased Learning to Rank
di: Liang, Zeyan, et al.
Pubblicazione: (2025)

Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition
di: Zhao, Haiyuan, et al.
Pubblicazione: (2022)

Following the Eye-Tracking Evidence: Established Web-Search Assumptions Fail in Carousel Interfaces
di: Kang, Jingwei, et al.
Pubblicazione: (2026)

Tutorial on Reasoning for IR & IR for Reasoning
di: Hoveyda, Mohanna, et al.
Pubblicazione: (2026)

Optimizing Generative Ranking Relevance via Reinforcement Learning in Xiaohongshu Search
di: Zeng, Ziyang, et al.
Pubblicazione: (2025)