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
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
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth 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
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Active feedback in ad hoc 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
Minimal test collections for retrieval evaluation
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing estimated loss reduction for active sampling in rank learning
Proceedings of the 25th international conference on Machine learning
A bayesian logistic regression model for active relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Actively Transfer Domain Knowledge
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Trada: tree based ranking function adaptation
Proceedings of the 17th ACM conference on Information and knowledge management
Dataset Shift in Machine Learning
Dataset Shift in Machine Learning
TransRank: A Novel Algorithm for Transfer of Rank Learning
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Deep versus shallow judgments in learning to rank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Query sampling for ranking learning in web search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A ranking approach to keyphrase extraction
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Ranking model adaptation for domain-specific search
Proceedings of the 18th ACM conference on Information and knowledge management
Expected reciprocal rank for graded relevance
Proceedings of the 18th ACM conference on Information and knowledge management
Heterogeneous cross domain ranking in latent space
Proceedings of the 18th ACM conference on Information and knowledge management
Model adaptation via model interpolation and boosting for web search ranking
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Proceedings of the ACM International Conference on Image and Video Retrieval
Knowledge transfer for cross domain learning to rank
Information Retrieval
Learning to rank only using training data from related domain
Proceedings of the 33rd 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
Learning to rank audience for behavioral targeting
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Multi-task learning for boosting with application to web search ranking
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Domain adaptation meets active learning
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
Hybrid active learning for cross-domain video concept detection
Proceedings of the international conference on Multimedia
A selective sampling strategy for label ranking
ECML'06 Proceedings of the 17th European conference on Machine Learning
Content-based retrieval for heterogeneous domains: domain adaptation by relative aggregation points
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Variance maximization via noise injection for active sampling in learning to rank
Proceedings of the 21st ACM international conference on Information and knowledge management
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Democracy is good for ranking: towards multi-view rank learning and adaptation in web search
Proceedings of the 7th ACM international conference on Web search and data mining
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Learning to adapt in a new setting is a common challenge to our knowledge and capability. New life would be easier if we actively pursued supervision from the right mentor chosen with our relevant but limited prior knowledge. This variant principle of active learning seems intuitively useful to many domain adaptation problems. In this paper, we substantiate its power for advancing automatic ranking adaptation, which is important in web search since it's prohibitive to gather enough labeled data for every search domain for fully training domain-specific rankers. For the cost-effectiveness, it is expected that only those most informative instances in target domain are collected to annotate while we can still utilize the abundant ranking knowledge in source domain. We propose a unified ranking framework to mutually reinforce the active selection of informative target-domain queries and the appropriate weighting of source training data as related prior knowledge. We select to annotate those target queries whose documents' order most disagrees among the members of a committee built on the mixture of source training data and the already selected target data. Then the replenished labeled set is used to adjust the importance of source queries for enhancing their rank transfer. This procedure iterates until labeling budget exhausts. Based on LETOR3.0 and Yahoo! Learning to Rank Challenge data sets, our approach significantly outperforms the random query annotation commonly used in ranking adaptation and the active rank learner on target-domain data only.