Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Truthful auctions for pricing search keywords
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
On a theory of learning with similarity functions
ICML '06 Proceedings of the 23rd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
The Journal of Machine Learning Research
Fast learning of document ranking functions with the committee perceptron
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Online learning from click data for sponsored search
Proceedings of the 17th international conference on World Wide Web
Contextual advertising by combining relevance with click feedback
Proceedings of the 17th international conference on World Wide Web
Efficient projections onto the l1-ball for learning in high dimensions
Proceedings of the 25th international conference on Machine learning
Predicting bounce rates in sponsored search advertisements
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse Online Learning via Truncated Gradient
The Journal of Machine Learning Research
Bagging gradient-boosted trees for high precision, low variance ranking models
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Interactive learning for efficiently detecting errors in insurance claims
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A simple methodology for soft cost-sensitive classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Cross-domain collaboration recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Real-time top-n recommendation in social streams
Proceedings of the sixth ACM conference on Recommender systems
Proceedings of the 21st ACM international conference on Information and knowledge management
Generating pseudo test collections for learning to rank scientific articles
CLEF'12 Proceedings of the Third international conference on Information Access Evaluation: multilinguality, multimodality, and visual analytics
Patent partner recommendation in enterprise social networks
Proceedings of the sixth ACM international conference on Web search and data mining
CTR prediction for contextual advertising: learning-to-rank approach
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
On the precision of social and information networks
Proceedings of the first ACM conference on Online social networks
Predicting response in mobile advertising with hierarchical importance-aware factorization machine
Proceedings of the 7th ACM international conference on Web search and data mining
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Many real-world data mining tasks require the achievement of two distinct goals when applied to unseen data: first, to induce an accurate preference ranking, and second to give good regression performance. In this paper, we give an efficient and effective Combined Regression and Ranking method (CRR) that optimizes regression and ranking objectives simultaneously. We demonstrate the effectiveness of CRR for both families of metrics on a range of large-scale tasks, including click prediction for online advertisements. Results show that CRR often achieves performance equivalent to the best of both ranking-only and regression-only approaches. In the case of rare events or skewed distributions, we also find that this combination can actually improve regression performance due to the addition of informative ranking constraints.