Cover Image

Theoretical Guarantees for LT-TTD: A Unified Transformer-based Architecture for Two-Level Ranking Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Abraich, Ayoub
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

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

TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis
by: Abraich, Ayoub
Published: (2025)

Continual Low-Rank Adapters for LLM-based Generative Recommender Systems
by: Yoo, Hyunsik, et al.
Published: (2025)

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

UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale
by: Li, Hanyu, et al.
Published: (2026)

A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization
by: Tan, Shiyin, et al.
Published: (2025)

Does Weighting Improve Matrix Factorization for Recommender Systems?
by: Ayoub, Alex, et al.
Published: (2025)

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

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

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

asLLR: LLM based Leads Ranking in Auto Sales
by: Sun, Yin, et al.
Published: (2025)

Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems
by: Wang, Siyu, et al.
Published: (2024)

COFFEE: COdesign Framework for Feature Enriched Embeddings in Ads-Ranking Systems
by: Roychowdhury, Sohini, et al.
Published: (2026)

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

A Unified Causal Framework for Auditing Recommender Systems for Ethical Concerns
by: Sharma, Vibhhu, et al.
Published: (2024)

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

Shielded RecRL: Explanation Generation for Recommender Systems without Ranking Degradation
by: Tiwari, Ansh, et al.
Published: (2025)

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

Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)
by: Li, Yuchen, et al.
Published: (2024)

NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
by: Zhang, Tunhou, et al.
Published: (2022)

Context Features Are Cheap: Rank-Aware Decomposition for Efficient Feature Interaction in Recommender Systems
by: Tkach, Yevgeny
Published: (2026)

Quantifying User Coherence: A Unified Framework for Analyzing Recommender Systems Across Domains
by: Soumm, Michaël, et al.
Published: (2024)

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)

End-to-End Personalization: Unifying Recommender Systems with Large Language Models
by: Ebrat, Danial, et al.
Published: (2025)

A Self-boosted Framework for Calibrated Ranking
by: Zhang, Shunyu, 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)

Incentive-Aware Recommender Systems in Two-Sided Markets
by: Dai, Xiaowu, et al.
Published: (2022)

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

Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systems
by: Xu, Wei, et al.
Published: (2025)

Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
by: Coleman, Benjamin, et al.
Published: (2023)

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

UnifiedRL: A Reinforcement Learning Algorithm Tailored for Multi-Task Fusion in Large-Scale Recommender Systems
by: Liu, Peng, et al.
Published: (2024)

BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale
by: Ardywibowo, Randy, et al.
Published: (2024)

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

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)

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

Correcting for Position Bias in Learning to Rank: A Control Function Approach
by: Islam, Md Aminul, et al.
Published: (2025)