Papers with Code 아카이브

Learning-To-Rank

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/thunlp/MetaAdaptRank

공식 코드

Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision

https://github.com/thunlp/ReInfoSelect

공식 코드

Selective Weak Supervision for Neural Information Retrieval

https://github.com/tianhewu/visualquality-r1

공식 코드

VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank

https://github.com/tomvek/mips-learnt-ivf

공식 코드

A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search

https://github.com/tonyby/ramqa

공식 코드

RAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question Answering

https://github.com/trustworthy-information-access/dualipw

공식 코드

Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and Relevance

https://github.com/tuetschek/ratpred

공식 코드

Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)

https://github.com/ultmaster/acenas

공식 코드

Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative Gain

https://github.com/uynaes/rankingawareclip

공식 코드

Ranking-aware adapter for text-driven image ordering with CLIP

https://github.com/vectara/mirage-bench

공식 코드

MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems

https://github.com/veneres/ilmart

공식 코드

ILMART: Interpretable Ranking with Constrained LambdaMART

https://github.com/verdurechen/group-qpp

공식 코드

Groupwise Query Performance Prediction with BERT

https://github.com/waylonli/tsprank-kdd2025

공식 코드

TSPRank: Bridging Pairwise and Listwise Methods with a Bilinear Travelling Salesman Model

https://github.com/wildltr/ptranking

공식 코드

PT-Ranking: A Benchmarking Platform for Neural Learning-to-Rank

https://github.com/xialeiliu/CrowdCountingCVPR18

공식 코드

Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

https://github.com/xiangxdai/combinatorial-logistic-bandit

공식 코드

Combinatorial Logistic Bandits

https://github.com/xpmir/cosplade

공식 코드

CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval

https://github.com/xsc1234/genrt

공식 코드

List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation

https://github.com/xuanyuan14/thuir_wsdm_cup

공식 코드

THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank

https://github.com/yandex/research-optimization-of-decision-tree-evaluation

공식 코드

Optimization of Decision Tree Evaluation Using SIMD Instructions

https://github.com/ygcinar/SmoothI

공식 코드

SmoothI: Smooth Rank Indicators for Differentiable IR Metrics

https://github.com/yiling-chen/flickr-cropping-dataset

공식 코드

Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study

https://github.com/yilingjia/PairRank

공식 코드

PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer

https://github.com/yxsu/LtR.jl

공식 코드

Learning to Rank Using Localized Geometric Mean Metrics

https://github.com/zalandoresearch/pairwise-debiasing

공식 코드

A General Framework for Pairwise Unbiased Learning to Rank

https://github.com/zeyuzhang1901/unified-off-policy-ltr-neurips2023

공식 코드

Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective

https://github.com/zhichaoxu-shufe/semantic-enhanced-bper

공식 코드

Learning to Rank Rationales for Explainable Recommendation

https://github.com/zhitao-wang/plnlp

공식 코드

Pairwise Learning for Neural Link Prediction

https://github.com/zwx8981/UNIQUE

공식 코드

Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild

https://github.com/zycdev/L2R2

공식 코드

L2R2: Leveraging Ranking for Abductive Reasoning

https://github.com/AdeDZY/K-NRM

End-to-End Neural Ad-hoc Ranking with Kernel Pooling

https://github.com/Azizimj/PriorOLTR

Overcoming Prior Misspecification in Online Learning to Rank

https://github.com/CyberAgentAILab/annotation-efficient-po

LiPO: Listwise Preference Optimization through Learning-to-Rank

https://github.com/LONG-9621/IQA_02

Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

https://github.com/LinJayan/DCN_V2_Paddle

DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

https://github.com/Museau/Survival_Pytorch_EMD

Learning to rank for censored survival data

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.