Image de couverture de livre

Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Wang, Yunli, Wang, Zhiqiang, Yang, Jian, Wen, Shiyang, Kong, Dongying, Li, Han, Gai, Kun
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Learning Cascade Ranking as One Network
par: Wang, Yunli, et autres
Publié: (2025)

Adaptive$^2$: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling
par: Sun, Wenxuan, et autres
Publié: (2024)

Differentiable Fast Top-K Selection for Large-Scale Recommendation
par: Zhu, Yanjie, et autres
Publié: (2025)

Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction
par: Fan, Zhongxiang, et autres
Publié: (2024)

Revenue Maximization and Learning in Products Ranking
par: Chen, Ningyuan, et autres
Publié: (2020)

Scaling Laws for Online Advertisement Retrieval
par: Wang, Yunli, et autres
Publié: (2024)

Learning Permutation Distributions via Reflected Diffusion on Ranks
par: He, Sizhuang, et autres
Publié: (2026)

Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach
par: Ghaffari, Fatemeh, et autres
Publié: (2025)

AIF: Asynchronous Inference Framework for Cost-Effective Pre-Ranking
par: Kou, Zhi, et autres
Publié: (2025)

FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment
par: Zaccone, Riccardo, et autres
Publié: (2026)

Adaptively Learning to Select-Rank in Online Platforms
par: Wang, Jingyuan, et autres
Publié: (2024)

CR-Net: Scaling Parameter-Efficient Training with Cross-Layer Low-Rank Structure
par: Kong, Boao, et autres
Publié: (2025)

Conditional Rank-Rank Regression via Deep Conditional Transformation Models
par: Wang, Xiaoyi, et autres
Publié: (2026)

Detecting and Ranking Causal Anomalies in End-to-End Complex System
par: Chang, Ching, et autres
Publié: (2023)

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization
par: Gao, Yihang, et autres
Publié: (2025)

COFFEE: COdesign Framework for Feature Enriched Embeddings in Ads-Ranking Systems
par: Roychowdhury, Sohini, et autres
Publié: (2026)

CaliCausalRank: Calibrated Multi-Objective Ad Ranking with Robust Counterfactual Utility Optimization
par: Yang, Xikai, et autres
Publié: (2026)

MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning
par: Zhang, Dacao, et autres
Publié: (2025)

Graph Contrastive Learning with Low-Rank Regularization and Low-Rank Attention for Noisy Node Classification
par: Wang, Yancheng, et autres
Publié: (2024)

QAMRO: Quality-aware Adaptive Margin Ranking Optimization for Human-aligned Assessment of Audio Generation Systems
par: Wang, Chien-Chun, et autres
Publié: (2025)

Blending Learning to Rank and Dense Representations for Efficient and Effective Cascades
par: Nardini, Franco Maria, et autres
Publié: (2025)

AdaRankGrad: Adaptive Gradient-Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning
par: Refael, Yehonathan, et autres
Publié: (2024)

Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation
par: Gomez, Camilo, et autres
Publié: (2026)

LambdaRankIC: Directly Optimizing Rank IC for Financial Prediction
par: Lin, Yan, et autres
Publié: (2026)

LoRA-GA: Low-Rank Adaptation with Gradient Approximation
par: Wang, Shaowen, et autres
Publié: (2024)

Learning Counterfactual Outcomes Under Rank Preservation
par: Wu, Peng, et autres
Publié: (2025)

A Disentangled Low-Rank RNN Framework for Uncovering Neural Connectivity and Dynamics
par: Li, Chengrui, et autres
Publié: (2025)

GoalRank: Group-Relative Optimization for a Large Ranking Model
par: Zhang, Kaike, et autres
Publié: (2025)

Accelerating the Low-Rank Decomposed Models
par: Hajimolahoseini, Habib, et autres
Publié: (2024)

Low-Rank Adaptation of Neural Fields
par: Truong, Anh, et autres
Publié: (2025)

SARA: Singular-Value Based Adaptive Low-Rank Adaption
par: Gu, Jihao, et autres
Publié: (2024)

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning
par: Feng, Jinyuan, et autres
Publié: (2025)

Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification
par: Wang, Zitai, et autres
Publié: (2024)

Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning
par: Zhang, Boning, et autres
Publié: (2024)

Generalization Bounds for Rank-sparse Neural Networks
par: Ledent, Antoine, et autres
Publié: (2025)

Towards Off-Policy Reinforcement Learning for Ranking Policies with Human Feedback
par: Xiao, Teng, et autres
Publié: (2024)

Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning
par: Zhao, Ziyu, et autres
Publié: (2025)

A Self-boosted Framework for Calibrated Ranking
par: Zhang, Shunyu, et autres
Publié: (2024)

MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning
par: Wang, Maolin, et autres
Publié: (2025)

Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization
par: Yao, Weichi, et autres
Publié: (2026)