IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Support vector machines are universally consistent
Journal of Complexity
Greedy algorithms for classification—consistency, convergence rates, and adaptivity
The Journal of Machine Learning Research
On the rate of convergence of regularized boosting classifiers
The Journal of Machine Learning Research
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
The Journal of Machine Learning Research
Generalization Bounds for the Area Under the ROC Curve
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Ranking and scoring using empirical risk minimization
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Learnability of bipartite ranking functions
COLT'05 Proceedings of the 18th annual conference on Learning Theory
On the consistency of multiclass classification methods
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Magnitude-preserving ranking algorithms
Proceedings of the 24th international conference on Machine learning
A regression framework for learning ranking functions using relative relevance judgments
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
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
Directly optimizing evaluation measures in learning to rank
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
ABC-boost: adaptive base class boost for multi-class classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Ranking with ordered weighted pairwise classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Early exit optimizations for additive machine learned ranking systems
Proceedings of the third ACM international conference on Web search and data mining
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List
The Journal of Machine Learning Research
Actively predicting diverse search intent from user browsing behaviors
Proceedings of the 19th international conference on World wide web
Gradient descent optimization of smoothed information retrieval metrics
Information Retrieval
Introduction to special issue on learning to rank for information retrieval
Information Retrieval
Efficient algorithms for ranking with SVMs
Information Retrieval
Active learning for ranking through expected loss optimization
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised ranking for document retrieval
Computer Speech and Language
A stochastic learning-to-rank algorithm and its application to contextual advertising
Proceedings of the 20th international conference on World wide web
Learning to rank for why-question answering
Information Retrieval
Information, Divergence and Risk for Binary Experiments
The Journal of Machine Learning Research
Ranking function adaptation with boosting trees
ACM Transactions on Information Systems (TOIS)
Leveraging Auxiliary Data for Learning to Rank
ACM Transactions on Intelligent Systems and Technology (TIST)
Flexible sample selection strategies for transfer learning in ranking
Information Processing and Management: an International Journal
A Learning to Rank framework applied to text-image retrieval
Multimedia Tools and Applications
On ranking and generalization bounds
The Journal of Machine Learning Research
Top-k learning to rank: labeling, ranking and evaluation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Information Sciences: an International Journal
Extractive speech summarization using evaluation metric-related training criteria
Information Processing and Management: an International Journal
Improving ranking performance with cost-sensitive ordinal classification via regression
Information Retrieval
| Hi-index | 0.02 |
We study the subset ranking problem, motivated by its important application in web-search. In this context, we consider the standard DCG criterion (discounted cumulated gain) that measures the quality of items near the top of the rank-list. Similar to error minimization for binary classification, the DCG criterion leads to a non-convex optimization problem that can be NP-hard. Therefore a computationally more tractable approach is needed. We present bounds that relate the approximate optimization of DCG to the approximate minimization of certain regression errors. These bounds justify the use of convex learning formulations for solving the subset ranking problem. The resulting estimation methods are not conventional, in that we focus on the estimation quality in the top-portion of the rank-list. We further investigate the generalization ability of these formulations. Under appropriate conditions, the consistency of the estimation schemes with respect to the DCG metric can be derived.