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
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.
Rank Pooling for Action Recognition
We show how the parameters of a function that has been fit to the video data can serve as a robust new video representation.
DCM Bandits: Learning to Rank with Multiple Clicks
This work presents the first practical and regret-optimal online algorithm for learning to rank with multiple clicks in a cascade-like click model.
Off-policy evaluation for slate recommendation
This paper studies the evaluation of policies that recommend an ordered set of items (e. g., a ranking) based on some context---a common scenario in web search, ads, and recommendation.
Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study
Automatic photo cropping is an important tool for improving visual quality of digital photos without resorting to tedious manual selection.
Match-Tensor: a Deep Relevance Model for Search
cspoh/IRDM2017
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The architecture of the Match-Tensor model simultaneously accounts for both local relevance matching and global topicality signals allowing for a rich interplay between them when computing the relevance of a document to a query.
Using Titles vs. Full-text as Source for Automated Semantic Document Annotation
For the first time, we offer a systematic comparison of classification approaches to investigate how far semantic annotations can be conducted using just the metadata of the documents such as titles published as labels on the Linked Open Data cloud.
Learning to Rank Using Localized Geometric Mean Metrics
First, we design a concept called \textit{ideal candidate document} to introduce metric learning algorithm to query-independent model.
Hashing as Tie-Aware Learning to Rank
Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval.
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
AdeDZY/K-NRM
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Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score.
