Learning-To-Rank
178 papers with code • 0 benchmarks • 9 datasets
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).
Benchmarks
These leaderboards are used to track progress in Learning-To-Rank
Libraries
Use these libraries to find Learning-To-Rank models and implementationsDatasets
MSLR-WEB10K
Learning to Rank Challenge
ReQA
Most implemented papers
THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment
lihaitao18375278/THUIR-COLIEE2023
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This paper describes the approach of the THUIR team at the COLIEE 2023 Legal Case Entailment task.
THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval
Legal case retrieval techniques play an essential role in modern intelligent legal systems.
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
zeyuzhang1901/unified-off-policy-ltr-neurips2023
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NeurIPS 2023
Building upon this, we leverage offline RL techniques for off-policy LTR and propose the Click Model-Agnostic Unified Off-policy Learning to Rank (CUOLR) method, which could be easily applied to a wide range of click models.
Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification
raywang335/l2rclip
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NeurIPS 2023
Consequently, we propose a cross-modal ordinal pairwise loss to refine the CLIP feature space, where texts and images maintain both semantic alignment and ordering alignment.
Learning to Rank in Generative Retrieval
However, only learning to generate is insufficient for generative retrieval.
Contextual Semibandits via Supervised Learning Oracles
We study an online decision making problem where on each round a learner chooses a list of items based on some side information, receives a scalar feedback value for each individual item, and a reward that is linearly related to this feedback.
More Accurate Question Answering on Freebase
Real-world factoid or list questions often have a simple structure, yet are hard to match to facts in a given knowledge base due to high representational and linguistic variability.
