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Learning-To-Rank

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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/him229/fultr

공식 코드

Policy-Gradient Training of Fair and Unbiased Ranking Functions

https://github.com/hpclab/efficient_nn_for_ltr

공식 코드

Distilled Neural Networks for Efficient Learning to Rank

https://github.com/hyz20/cld

공식 코드

Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition

https://github.com/ielab/2024-ecir-foltr-unlearning

공식 코드

How to Forget Clients in Federated Online Learning to Rank?

https://github.com/ielab/non-iid-foltr

공식 코드

Is Non-IID Data a Threat in Federated Online Learning to Rank?

https://github.com/ielab/oltr

공식 코드

Reinforcement Online Learning to Rank with Unbiased Reward Shaping

https://github.com/iris1026/unlearning-for-foltr

공식 코드

Unlearning for Federated Online Learning to Rank: A Reproducibility Study

https://github.com/irslu/ltrp

공식 코드

Learning to Rank Patches for Unbiased Image Redundancy Reduction

https://github.com/ita9naiwa/rlrd-aaai2022

공식 코드

An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation

https://github.com/jayman91/ltr-predopt

공식 코드

Decision-Focused Learning: Through the Lens of Learning to Rank

https://github.com/jeon185/leaprec

공식 코드

Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential Recommendation

https://github.com/jgonsior/DS22-ImitAL

공식 코드

ImitAL: Learned Active Learning Strategy on Synthetic Data

https://github.com/jgonsior/ImitAL

공식 코드

ImitAL: Learning Active Learning Strategies from Synthetic Data

https://github.com/jkang98/2024-sigir-xgboost-pl-rank

공식 코드

Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees

https://github.com/keio-smilab23/multirankit

공식 코드

Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine

https://github.com/keytoyze/ultr-identifiability

공식 코드

Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank

https://github.com/keytoyze/vectorization

공식 코드

Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank

https://github.com/kimdanny/starlight-liverag

공식 코드

LTRR: Learning To Rank Retrievers for LLMs

https://github.com/kominaru/brie

공식 코드

Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for Explainability

https://github.com/kramerlab/direct-ranker

공식 코드

Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance

https://github.com/lamda-bbo/offline-ram

공식 코드

Offline Model-Based Optimization by Learning to Rank

https://github.com/lihaitao18375278/THUIR-COLIEE2023

공식 코드

THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment

https://github.com/lileipisces/EXTRA

공식 코드

On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance

https://github.com/lileipisces/bper

공식 코드

On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance

https://github.com/liuqi6777/pe_rank

공식 코드

Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models

https://github.com/liuyuqi123study/ranksvm_for_slr

공식 코드

Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM

https://github.com/liyongqi67/ltrgr

공식 코드

Learning to Rank in Generative Retrieval

https://github.com/liyuan9988/UncoupledRegressionComparison

공식 코드

Uncoupled Regression from Pairwise Comparison Data

https://github.com/luka-group/lite

공식 코드

Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference

https://github.com/maartenbuyl/rankformer

공식 코드

RankFormer: Listwise Learning-to-Rank Using Listwide Labels

https://github.com/maddalena-amendola/TUEF

공식 코드

Leveraging Topic Specificity and Social Relationships for Expert Finding in Community Question Answering Platforms

https://github.com/mariaheuss/rankingshap

공식 코드

RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models

https://github.com/masoudmansoury/ealinucb

공식 코드

Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits

https://github.com/microsoft/distPURL

공식 코드

Distance-based Positive and Unlabeled Learning for Ranking

https://github.com/mkachuee/Opportunistic

공식 코드

Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams

https://github.com/naver/sardine

공식 코드

SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments

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