Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper
Query: Using boosted trees for click-through rate prediction for sponsored search
Predicting clicks: estimating the click-through rate for new ads
Click Fraud Resistant Methods for Learning Click-Through Rates
XGBoost : Reliable Large-scale Tree Boosting System
XGBoost: A Scalable Tree Boosting System
Ad Recommendation for Sponsored Search Engine via Composite Long-Short Term Memory
Predicting ad click-through rates via feature-based fully coupled interaction tensor factorization
Learning relevance from click data via neural network based similarity models
A cascade model for externalities in sponsored search
Deep Reinforcement Learning for Sponsored Search Real-time Bidding
An Efficient Boosting Algorithm for Combining Preferences
Optimizing search engines using clickthrough data
An Online Learning Framework for Refining Recency Search Results with User Click Feedback
Improving quality of training data for learning to rank using click-through data
The sum of its parts: reducing sparsity in click estimation with query segments
A Neural Click Model for Web Search
Learning to Rank Microblog Posts for Real-Time Ad-Hoc Search
A Novel Ensemble Learning-Based Approach for Click Fraud Detection in Mobile Advertising
A scalable purchase intention prediction system using extreme gradient boosting machines with browsing content entropy
Learning Tree Augmented Naive Bayes for Ranking
Spatio-temporal models for estimating click-through rate
Geovisualization of Dynamics, Movement and Change: Key Issues and Developing Approaches in Visualization Research
Practical Bayesian optimization in the presence of outliers
Strategies for Reducing and Correcting OCR Errors
Improving Regressors using Boosting Techniques
Hardware/Software Co-Design of an Accelerator for FV Homomorphic Encryption Scheme Using Karatsuba Algorithm