Modern Information Retrieval
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
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
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
Query dependent ranking using K-nearest neighbor
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
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
Heterogeneous cross domain ranking in latent space
Proceedings of the 18th ACM conference on Information and knowledge management
Domain adaptation via transfer component analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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
A theory of learning from different domains
Machine Learning
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
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Transferring knowledge with source selection to learn IR functions on unlabeled collections
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.01 |
We propose to directly measure the importance of queries in the source domain to the target domain where no rank labels of documents are available, which is referred to as query weighting. Query weighting is a key step in ranking model adaptation. As the learning object of ranking algorithms is divided by query instances, we argue that it's more reasonable to conduct importance weighting at query level than document level. We present two query weighting schemes. The first compresses the query into a query feature vector, which aggregates all document instances in the same query, and then conducts query weighting based on the query feature vector. This method can efficiently estimate query importance by compressing query data, but the potential risk is information loss resulted from the compression. The second measures the similarity between the source query and each target query, and then combines these fine-grained similarity values for its importance estimation. Adaptation experiments on LETOR3.0 data set demonstrate that query weighting significantly outperforms document instance weighting methods.