Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
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
A continuation method for semi-supervised SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
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
Learning to rank with SoftRank and Gaussian processes
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Structured learning for non-smooth ranking losses
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
On the local optimality of LambdaRank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Smoothing DCG for learning to rank: a novel approach using smoothed hinge functions
Proceedings of the 18th ACM conference on Information and knowledge management
On the choice of effectiveness measures for learning to rank
Information Retrieval
Subset ranking using regression
COLT'06 Proceedings of the 19th annual conference on Learning Theory
A stochastic learning-to-rank algorithm and its application to contextual advertising
Proceedings of the 20th international conference on World wide web
Parallel boosted regression trees for web search ranking
Proceedings of the 20th international conference on World wide web
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
A robust ranking methodology based on diverse calibration of AdaBoost
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Smoothing NDCG metrics using tied scores
Proceedings of the 20th ACM international conference on Information and knowledge management
Maximum margin ranking algorithms for information retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Foundations and Trends in Information Retrieval
TFMAP: optimizing MAP for top-n context-aware recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Learning to rank social update streams
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Multiple objective optimization in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
Proceedings of the sixth ACM conference on Recommender systems
Direct optimization of ranking measures for learning to rank models
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
GAPfm: optimal top-n recommendations for graded relevance domains
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
CLiMF: collaborative less-is-more filtering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Most ranking algorithms are based on the optimization of some loss functions, such as the pairwise loss. However, these loss functions are often different from the criteria that are adopted to measure the quality of the web page ranking results. To overcome this problem, we propose an algorithm which aims at directly optimizing popular measures such as the Normalized Discounted Cumulative Gain and the Average Precision. The basic idea is to minimize a smooth approximation of these measures with gradient descent. Crucial to this kind of approach is the choice of the smoothing factor. We provide various theoretical analysis on that choice and propose an annealing algorithm to iteratively minimize a less and less smoothed approximation of the measure of interest. Results on the Letor benchmark datasets show that the proposed algorithm achieves state-of-the-art performances.