Cover Image

A Self-boosted Framework for Calibrated Ranking

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Shunyu, Liu, Hu, Bao, Wentian, Yu, Enyun, Song, Yang
Format:	Preprint
Published:	2024
Subjects:	Information Retrieval Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Unconstrained Monotonic Calibration of Predictions in Deep Ranking Systems
by: Bai, Yimeng, et al.
Published: (2025)

Beyond Relevance: Improving User Engagement by Personalization for Short-Video Search
by: Bao, Wentian, et al.
Published: (2024)

GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction
by: Lin, Zhijie, et al.
Published: (2025)

DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models
by: Li, Wuchao, et al.
Published: (2024)

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

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

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

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

Isotonic Layer: A Unified Framework for Recommendation Calibration and Debiasing
by: Cheng, Hailing, et al.
Published: (2026)

Hierarchical Group-wise Ranking Framework for Recommendation Models
by: Yan, YaChen, et al.
Published: (2025)

AIF: Asynchronous Inference Framework for Cost-Effective Pre-Ranking
by: Kou, Zhi, et al.
Published: (2025)

A Collaborative Ensemble Framework for CTR Prediction
by: Liu, Xiaolong, et al.
Published: (2024)

Deferred is Better: A Framework for Multi-Granularity Deferred Interaction of Heterogeneous Features
by: Xu, Yi, 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)

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

Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random
by: Kweon, Wonbin, et al.
Published: (2024)

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

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

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

Posterior Probability Matters: Doubly-Adaptive Calibration for Neural Predictions in Online Advertising
by: Wei, Penghui, et al.
Published: (2022)

Hierarchical Spatial-Temporal Graph-Enhanced Model for Map-Matching
by: Gao, Anjun, et al.
Published: (2026)

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

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

Addressing Information Loss and Interaction Collapse: A Dual Enhanced Attention Framework for Feature Interaction
by: Xu, Yi, et al.
Published: (2025)

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

Joint Model Parameter Scaling and Universal-Domain Data Integration for E-commerce Search Ranking
by: Yu, Liren, 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)

Low-Rank Online Dynamic Assortment with Dual Contextual Information
by: Lee, Seong Jin, et al.
Published: (2024)

Calibrated Recommendations with Contextual Bandits
by: Feijer, Diego, et al.
Published: (2025)

UDuo: Universal Dual Optimization Framework for Online Matching
by: Li, Bin, 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)

Calibrating the Predictions for Top-N Recommendations
by: Sato, Masahiro
Published: (2024)

RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs
by: Li, Yuan, et al.
Published: (2025)

Beyond Self-Consistency: Loss-Balanced Perturbation-Based Regularization Improves Industrial-Scale Ads Ranking
by: Ramazanli, Ilqar, 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)

Understanding Distribution Structure on Calibrated Recommendation Systems
by: da Silva, Diego Correa, et al.
Published: (2025)

Uncertainty-Calibrated Recommendations for Low-Active Users
by: Zou, Bob Junyi, et al.
Published: (2026)

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