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About this product
- DescriptionIn this work, Metzler describes highly effective information retrieval models for both smaller, classical data sets, and larger Web collections. In a shift away from heuristic, hand-tuned ranking functions and complex probabilistic models, he presents feature-based retrieval models. As he shows, combining term dependencies and arbitrary features results in a very robust, powerful retrieval model.
- Author BiographyDonald Metzler is a Research Scientist in the Natural Language Group at the University of Southern California's Information Sciences Institute. Prior to that he was a Research Scientist in the Search and Computational Advertising group at Yahoo! Research. He received his Ph.D. from the University of Massachusetts in 2007. He is an active member of the information retrieval and Web search communities, having served on the program committees of SIGIR, WWW, WSDM, HLT, EMNLP, and ICML. He has published over 35 research papers, and has 16 patents pending. His research interests include information retrieval, Web search, computational advertising, and applications of machine learning to large-scale text problems.
- Author(s)Donald Metzler
- PublisherSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
- Date of Publication04/09/2011
- GenreComputing: Professional & Programming
- Series TitleThe Information Retrieval Series
- Series Part/Volume Number27
- Place of PublicationBerlin
- Country of PublicationGermany
- ImprintSpringer-Verlag Berlin and Heidelberg GmbH & Co. K
- Content NoteXII, 168 p.
- Weight438 g
- Width155 mm
- Height235 mm
- Edition Statement2012
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