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Learning-To-Rank

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).

Papers

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Showing 26–50 of 753 papers

TitleStatusHype
Efficient support ticket resolution using Knowledge Graphs—0
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks—0
Effective and secure federated online learning to rank—0
Ranking-aware adapter for text-driven image ordering with CLIPCode1
Learning Cluster Representatives for Approximate Nearest Neighbor SearchCode0
GABAR: Graph Attention-Based Action Ranking for Relational Policy Learning—0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
ICLERB: In-Context Learning Embedding and Reranker Benchmark—0
LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System—0
A Survey on E-Commerce Learning to Rank—0
TSPRank: Bridging Pairwise and Listwise Methods with a Bilinear Travelling Salesman ModelCode1
An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking—0
Learning to Rank Salient Content for Query-focused Summarization—0
Don't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification—0
Combinatorial Logistic BanditsCode0
Optimizing Preference Alignment with Differentiable NDCG Ranking—0
MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation SystemsCode1
Offline Model-Based Optimization by Learning to RankCode1
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization—0
COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework—0
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale—0
Scale-Invariant Learning-to-Rank—0
Meta Learning to Rank for Sparsely Supervised Queries—0
Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation—0
Towards More Relevant Product Search Ranking Via Large Language Models: An Empirical Study—0
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