OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Value Regularization and Fenchel Duality
The Journal of Machine Learning Research
Learning to rank: from pairwise approach to listwise approach
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
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
An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Robust sparse rank learning for non-smooth ranking measures
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Efficient algorithms for ranking with SVMs
Information Retrieval
Aggregating preference ranking with fuzzy Data Envelopment Analysis
Knowledge-Based Systems
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Learning to rank with document ranks and scores
Knowledge-Based Systems
Transductive learning to rank using association rules
Expert Systems with Applications: An International Journal
IEEE Transactions on Fuzzy Systems
Sparse Learning-to-Rank via an Efficient Primal-Dual Algorithm
IEEE Transactions on Computers
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Recently, learning-to-rank has attracted considerable attention. Although significant research efforts have been focused on learning-to-rank, it is not the case for the problem of learning sparse models for ranking. In this paper, we consider the sparse learning-to-rank problem. We formulate it as an optimization problem with the @?"1 regularization, and develop a simple but efficient iterative algorithm to solve the optimization problem. Experimental results on four benchmark datasets demonstrate that the proposed algorithm shows (1) superior performance gain compared to several state-of-the-art learning-to-rank algorithms, and (2) very competitive performance compared to FenchelRank that also learns a sparse model for ranking.