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Learning to rank
Context-Aware Learning to Rank with Self-Attention
An Introduction to Neural Information Retrieval
Part I
Metric Learning for Session-Based Recommendations - Preprint
Learning Embeddings: Efficient Algorithms and Applications
An Empirical Study of Embedding Features in Learning to Rank
Learning to Rank Using Gradient Descent
Multi-Agent Reinforced Learning to Rank
Yahoo! Learning to Rank Challenge Overview
Sodeep: a Sorting Deep Net to Learn Ranking Loss Surrogates
Spectrum-Enhanced Pairwise Learning to Rank
Embedding Meta-Textual Information for Improved Learning to Rank
Self-Supervised Learning from Permutations Via Differentiable Ranking
Online Learning to Rank with Features
Learning to Explain Entity Relationships by Pairwise Ranking with Convolutional Neural Networks
Listwise Learning to Rank by Exploring Unique Ratings
Deep Learning for Music: Similarity Search and Beyond
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search
Top View
Learning to Rank Retrieval Results for Geographically Constrained Search Queries
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval
An Introduction to Neural Information Retrieval
Optimize Sequencewise Learning to Rank Using Squeeze-And-Excitation Network
Learning to Rank Learning Curves
Learning to Rank with Deep Neural Networks
Reinforcement Learning to Rank with Markov Decision Process
Learning to Rank by Optimizing NDCG Measure
A Relaxed Ranking-Based Factor Model for Recommender System
Learning to Rank for Synthesizing Planning Heuristics
Deep Multi-View Learning to Rank
Ranking Methods in Machine Learning
Extreme Learning to Rank Via Low Rank Assumption
Deep Reinforcement Learning
Listwise Neural Ranking Models
Learning to Rank, a Supervised Approach for Ranking of Documents Master Thesis in Computer Science - Algorithms, Languages and Logic
End-To-End Neural Ad-Hoc Ranking with Kernel Pooling
Towards Recommendation with User Action Sequences
Learning to Rank: Applications to Bioinformatics Hiroshi Mamitsuka Kyoto University & Aalto University
Unsupervised and Supervised Embeddings
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance
Large Scale Learning to Rank
Deep Learning for Audio and Music
University of Glasgow Terrier Team at the TREC 2020 Deep Learning Track
Ranking Measures and Loss Functions in Learning to Rank
Ordinal Non-Negative Matrix Factorization for Recommendation Olivier Gouvert, Thomas Oberlin, Cédric Févotte
Learning to Rank for Information Retrieval and Natural Language Processing
Learning to Rank
Data Augmentation Based on Adversarial Autoencoder Handling Imbalance for Learning to Rank∗
Learning to Rank: Online Learning, Statistical Theory and Applications
Multimodal Machine Learning: a Survey and Taxonomy
Learning to Rank with Click-Through Features in a Reinforcement Learning Framework
Learning to Rank
Learning to Rank: from Pairwise Approach to Listwise Approach
Learning to Rank
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
Lambdamf: Learning Nonsmooth Ranking Functions in Matrix Factorization Using Lambda
Learning to Rank for Collaborative Filtering
Deep Metric Learning to Rank
Learning to Rank with Deep Autoencoder Features
Revisiting Non-Linear Matrix Factorization for Learning
Learning to Rank for Consumer Health Search: a Semantic Approach
A General Framework for Counterfactual Learning-To-Rank
Learning to Rank Model Performance and Review with Pairwise Transformations
Deeprank: a New Deep Architecture for Relevance Ranking in Information Retrieval
An Attention-Based Deep Net for Learning to Rank
A Short Introduction to Learning to Rank
Balancing Exploration and Exploitation in Listwise and Pairwise Online Learning to Rank for Information Retrieval
Learning to Rank for Information Retrieval Contents
Context-Aware Learning to Rank with Self-Attention