SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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
Query dependent ranking using K-nearest neighbor
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Ambiguous queries: test collections need more sense
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Multiple approaches to analysing query diversity
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
Estimating the Query Difficulty for Information Retrieval
Estimating the Query Difficulty for Information Retrieval
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Selectively diversifying web search results
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Quality-biased ranking of web documents
Proceedings of the fourth ACM international conference on Web search and data mining
Parallel boosted regression trees for web search ranking
Proceedings of the 20th international conference on World wide web
Bagging gradient-boosted trees for high precision, low variance ranking models
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
The static absorbing model for the web
Journal of Web Engineering
Efficient and effective spam filtering and re-ranking for large web datasets
Information Retrieval
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
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Learning to rank studies have mostly focused on query-dependent and query-independent document features, which enable the learning of ranking models of increased effectiveness. Modern learning to rank techniques based on regression trees can support query features, which are document-independent, and hence have the same values for all documents being ranked for a query. In doing so, such techniques are able to learn sub-trees that are specific to certain types of query. However, it is unclear which classes of features are useful for learning to rank, as previous studies leveraged anonymised features. In this work, we examine the usefulness of four classes of query features, based on topic classification, the history of the query in a query log, the predicted performance of the query, and the presence of concepts such as persons and organisations in the query. Through experiments on the ClueWeb09 collection, our results using a state-of-the-art learning to rank technique based on regression trees show that all four classes of query features can significantly improve upon an effective learned model that does not use any query feature.