Machine Learning - Special issue on inductive transfer
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Empirical Bayes for Learning to Learn
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Efficient Boosting Algorithm for Combining Preferences
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
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to learn with the informative vector machine
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Magnitude-preserving ranking algorithms
Proceedings of the 24th international conference on Machine learning
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Self-taught learning: transfer learning from unlabeled data
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
A regression framework for learning ranking functions using relative relevance judgments
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
Proceedings of the 25th international conference on Machine learning
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
A boosting algorithm for learning bipartite ranking functions with partially labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Directly optimizing evaluation measures in learning to rank
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank with partially-labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank with SoftRank and Gaussian processes
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Trada: tree based ranking function adaptation
Proceedings of the 17th ACM conference on Information and knowledge management
TransRank: A Novel Algorithm for Transfer of Rank Learning
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
An efficient algorithm for learning to rank from preference graphs
Machine Learning
Heterogeneous cross domain ranking in latent space
Proceedings of the 18th ACM conference on Information and knowledge management
Multi-task learning for learning to rank in web search
Proceedings of the 18th ACM conference on Information and knowledge management
IEEE Transactions on Knowledge and Data Engineering
Subset ranking using regression
COLT'06 Proceedings of the 19th annual conference on Learning Theory
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In learning to rank, both the quality and quantity of the training data have significant impacts on the performance of the learned ranking functions. However, in many applications, there are usually not sufficient labeled training data for the construction of an accurate ranking model. It is therefore desirable to leverage existing training data from other tasks when learning the ranking function for a particular task, an important problem which we tackle in this article utilizing a boosting framework with transfer learning. In particular, we propose to adaptively learn transferable representations called super-features from the training data of both the target task and the auxiliary task. Those super-features and the coefficients for combining them are learned in an iterative stage-wise fashion. Unlike previous transfer learning methods, the super-features can be adaptively learned by weak learners from the data. Therefore, the proposed framework is sufficiently flexible to deal with complicated common structures among different learning tasks. We evaluate the performance of the proposed transfer learning method for two datasets from the Letor collection and one dataset collected from a commercial search engine, and we also compare our methods with several existing transfer learning methods. Our results demonstrate that the proposed method can enhance the ranking functions of the target tasks utilizing the training data from the auxiliary tasks.