Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
ICML '04 Proceedings of the twenty-first 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
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning to rank: from pairwise approach to listwise approach
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
SoftRank: optimizing non-smooth rank metrics
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Query-level loss functions for information retrieval
Information Processing and Management: an International Journal
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
Learning to rank with partially-labeled data
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
Dataset Shift in Machine Learning
Dataset Shift in Machine Learning
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
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
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
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
Multi-view transfer learning with a large margin approach
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Multiview semi-supervised learning for ranking multilingual documents
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Ranking Model Adaptation for Domain-Specific Search
IEEE Transactions on Knowledge and Data Engineering
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Web search ranking models are learned from features originated from different views or perspectives of document relevancy, such as query dependent or independent features. This seems intuitively conformant to the principle of multi-view approach that leverages distinct complementary views to improve model learning. In this paper, we aim to obtain optimal separation of ranking features into non-overlapping subsets (i.e., views), and use such different views for rank learning and adaptation. We present a novel semi-supervised multi-view ranking model, which is then extended into an adaptive ranker for search domains where no training data exists. The core idea is to proactively strengthen view consistency (i.e., the consistency between different rankings each predicted by a distinct view-based ranker) especially when training and test data follow divergent distributions. For this purpose, we propose a unified framework based on listwise ranking scheme to mutually reinforce the view consistency of target queries and the appropriate weighting of source queries that act as prior knowledge. Based on LETOR and Yahoo Learning to Rank datasets, our method significantly outperforms some strong baselines including single-view ranking models commonly used and multi-view ranking models that do not impose view consistency on target data.