COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A sequential algorithm for training text classifiers
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)
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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
Active learning of label ranking functions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Linear discriminant model for information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
SVM selective sampling for ranking with application to data retrieval
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd 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
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ECML '07 Proceedings of the 18th European conference on Machine Learning
Hinge Rank Loss and the Area Under the ROC Curve
ECML '07 Proceedings of the 18th European conference on Machine Learning
Representative sampling for text classification using support vector machines
ECIR'03 Proceedings of the 25th European conference on IR research
A selective sampling strategy for label ranking
ECML'06 Proceedings of the 17th European conference on Machine Learning
Fast active exploration for link-based preference learning using Gaussian processes
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Balancing exploration and exploitation in learning to rank online
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Relevant knowledge helps in choosing right teacher: active query selection for ranking adaptation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Rule-based active sampling for learning to rank
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Active associative sampling for author name disambiguation
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Information Sciences: an International Journal
Two-Stage learning to rank for information retrieval
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
The whens and hows of learning to rank for web search
Information Retrieval
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Learning ranking functions is crucial for solving many problems, ranging from document retrieval to building recommendation systems based on an individual user's preferences or on collaborative filtering. Learning-to-rank is particularly necessary for adaptive or personalizable tasks, including email prioritization, individualized recommendation systems, personalized news clipping services and so on. Whereas the learning-to-rank challenge has been addressed in the literature, little work has been done in an active-learning framework, where requisite user feedback is minimized by selecting only the most informative instances to train the rank learner. This paper addresses active rank-learning head on, proposing a new sampling strategy based on minimizing hinge rank loss, and demonstrating the effectiveness of the active sampling method for rankSVM on two standard rank-learning datasets. The proposed method shows convincing results in optimizing three performance metrics, as well as improvement against four baselines including entropy-based, divergence- based, uncertainty-based and random sampling methods.