On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
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
Convex Optimization
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning to rank: from pairwise approach to listwise approach
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
FRank: a ranking method with fidelity loss
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
SoftRank: optimizing non-smooth rank metrics
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
Proximal regularization for online and batch learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Smoothing DCG for learning to rank: a novel approach using smoothed hinge functions
Proceedings of the 18th ACM conference on Information and knowledge management
Learning to Rank for Information Retrieval
Learning to Rank for Information Retrieval
Statistical Analysis of Bayes Optimal Subset Ranking
IEEE Transactions on Information Theory
Experiences with using SVM-based learning for multi-objective ranking
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning to blend rankings: a monotonic transformation to blend rankings from heterogeneous domains
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Estimating ad group performance in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
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Ranking a set of retrieved documents according to their relevance to a given query has become a popular problem at the intersection of web search, machine learning, and information retrieval. Recent work on ranking focused on a number of different paradigms, namely, pointwise, pairwise, and list-wise approaches. Each of those paradigms focuses on a different aspect of the dataset while largely ignoring others. The current paper shows how a combination of them can lead to improved ranking performance and, moreover, how it can be implemented in log-linear time. The basic idea of the algorithm is to use isotonic regression with adaptive bandwidth selection per relevance grade. This results in an implicitly-defined loss function which can be minimized efficiently by a subgradient descent procedure. Experimental results show that the resulting algorithm is competitive on both commercial search engine data and publicly available LETOR data sets.