Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper
Query: McRank: Learning to Rank Using Multiple Classification and Gradient Boosting
Learning to rank using gradient descent
Learning to rank for recommender systems
A feature selection method based on minimum redundancy maximum relevance for learning to rank
Deep Multi-view Learning to Rank
LETOR: A benchmark collection for research on learning to rank for information retrieval
Incorporating query-specific feedback into learning-to-rank models
FSMRank: Feature Selection Algorithm for Learning to Rank
Multi-task learning for boosting with application to web search ranking
Learning to rank recommendations with the k-order statistic loss
Learning Tree Augmented Naive Bayes for Ranking
Improving quality of training data for learning to rank using click-through data
Learning to Rank from Noisy Data
Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets
A pairwise ranking based approach to learning with positive and unlabeled examples
Learning to Rank with Selection Bias in Personal Search
Learning to Rank Using Localized Geometric Mean Metrics
Document selection methodologies for efficient and effective learning-to-rank
Semi-Supervised Ensemble Ranking
Learning to efficiently rank on big data
A regression framework for learning ranking functions using relative relevance judgments
Bagging Predictors
Cyclodextrins as Drug Carrier Molecule: A Review
A new pulsed power supply topology based on positive buck-boost converters concept
Cell death and immunity: The ABCs of granule-mediated cytotoxicity: new weapons in the arsenal
Investigation of the Bioaccessibility of Progesterone (Micronized and Non- micronized), Using an In Vitro Model of the Human Gastrointestinal System