A Bayesian/Information Theoretic Model of Learning to Learn viaMultiple Task Sampling
Machine Learning - Special issue on inductive transfer
Machine Learning - Special issue on inductive transfer
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Computational Statistics & Data Analysis - Nonlinear methods and data mining
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
Learning to rank using gradient descent
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 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
Cross-market model adaptation with pairwise preference data for web search ranking
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Fractional similarity: cross-lingual feature selection for search
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Multi-task learning to rank for web search
Pattern Recognition Letters
Leveraging Auxiliary Data for Learning to Rank
ACM Transactions on Intelligent Systems and Technology (TIST)
Efficient online learning for multitask feature selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Both the quality and quantity of training data have significant impact on the performance of ranking functions in the context of learning to rank for web search. Due to resource constraints, training data for smaller search engine markets are scarce and we need to leverage existing training data from large markets to enhance the learning of ranking function for smaller markets. In this paper, we present a boosting framework for learning to rank in the multi-task learning context for this purpose. In particular, we propose to learn non-parametric common structures adaptively from multiple tasks in a stage-wise way. An algorithm is developed to iteratively discover super-features that are effective for all the tasks. The estimation of the functions for each task is then learned as a linear combination of those super-features. We evaluate the performance of this multi-task learning method for web search ranking using data from a search engine. Our results demonstrate that multi-task learning methods bring significant relevance improvements over existing baseline methods.