Selective Sampling Using the Query by Committee Algorithm
Machine Learning
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
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
SVM selective sampling for ranking with application to data retrieval
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
SoftRank: optimizing non-smooth rank metrics
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Optimizing estimated loss reduction for active sampling in rank learning
Proceedings of the 25th international conference on Machine learning
Deep versus shallow judgments in learning to rank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in 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
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
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Active learning for ranking, which is to selectively label the most informative examples, has been widely studied in recent years. In this paper, we propose a general active learning for ranking strategy called Variance Maximization (VM). The algorithm relies on noise injection to perturb the original unlabeled examples and generate the rank distribution of each example. Using a DCG-like gain function to measure each ranked list sampled from the rank distribution, Variance Maximization selects the unlabeled example with the largest variance in the gain. The VM strategy is applied at both the query level and the document level, and a two-stage active learning algorithm is further derived. Experimental results on both the LETOR 4.0 dataset and a real-world Web search ranking dataset have demonstrated the effectiveness of the proposed active learning approach.