An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
A Markov random field model for term dependencies
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
Linear feature-based models for information retrieval
Information Retrieval
Latent concept expansion using markov random fields
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Incorporating term dependency in the dfr framework
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Effective top-k computation in retrieving structured documents with term-proximity support
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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
On the local optimality of LambdaRank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Document selection methodologies for efficient and effective learning-to-rank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Improving web search relevance with semantic features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Learning concept importance using a weighted dependence model
Proceedings of the third ACM international conference on Web search and data mining
On the choice of effectiveness measures for learning to rank
Information Retrieval
Adapting boosting for information retrieval measures
Information Retrieval
Quality-biased ranking of web documents
Proceedings of the fourth ACM international conference on Web search and data mining
Parameterized concept weighting in verbose queries
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Effective query formulation with multiple information sources
Proceedings of the fifth ACM international conference on Web search and data mining
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Current learning to rank approaches commonly focus on learning the best possible ranking function given a small fixed set of documents. This document set is often retrieved from the collection using a simple unsupervised bag-of-words method, e.g. BM25. This can potentially lead to learning a sub-optimal ranking, since many relevant documents may be excluded from the initially retrieved set. In this paper we propose a novel two-stage learning framework to address this problem. We first learn a ranking function over the entire retrieval collection using a limited set of textual features including weighted phrases, proximities and expansion terms. This function is then used to retrieve the best possible subset of documents over which the final model is trained using a larger set of query- and document-dependent features. Empirical evaluation using two web collections unequivocally demonstrates that our proposed two-stage framework, being able to learn its model from more relevant documents, outperforms current learning to rank approaches.