Information Retrieval and Data Mining
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Information retrieval research involves techniques from machine learning and other theoretical models, together with extensive experimentation to develop more accurate, fast and advanced information retrieval and search techniques for a variety of applications. |
Research Areas
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Retrieval Models |
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New features |
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Optimization and Learning |
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Measurement and Effectiveness |
Publications
- Filip Radlinski, Martin Szummer, and Nick Craswell, Inferring Query Intent from Reformulations and Clicks, in Proc. 19th Annual International World Wide Web Conference (WWW '10)., Association for Computing Machinery, Inc., April 2010
- Filip Radlinski, Martin Szummer, and Nick Craswell, Metrics for Assessing Sets of Subtopics, in SIGIR Conf. Research and Development in Information Retrieval, Association for Computing Machinery, Inc., July 2010
- Filip Radlinski and Nick Craswell, Comparing the Sensitivity of Information Retrieval Metrics, in Proceedings of SIGIR, Association for Computing Machinery, Inc., July 2010
- Fan Guo, Chao Liu, Anitha Kannan, Tom Minka, Michael Taylor, Yi-Min Wang, and Christos Faloutsos, Click Chain Model in Web Search, in WWW'09: Proceedings of the 18th International World Wide Web Conference, Association for Computing Machinery, Inc., April 2009
- Deepayan Chakrabarti, Ravi Kumar, Filip Radlinski, and Eli Upfal, Mortal Multi-Armed Bandits, in Advances in Neural Information Processing Systems (NIPS), 2009
- Onno Zoeter, Michael Taylor, Ed Snelson, John Guiver, Nick Craswell, and Martin Szummer, A Decision Theoretic Framework for Ranking using Implicit Feedback, in SIGIR 2008 Workshop on Learning to Rank for Information Retrieval, July 2008
- Hengzhi Wu, Gabriella Kazai, and Michael Taylor, Book Search Experiments: Investigating IR Methods for the Indexing and Retrieval of Books, in Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008, Springer, April 2008
- Michael Taylor, John Guiver, Stephen Robertson, and Tom Minka, SoftRank: Optimising Non-Smooth Rank Metrics, in WSDM 2008, February 2008
- Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey, An experimental comparison of click position-bias models, in WSDM '08: Proceedings of the international conference on Web search and web data mining, ACM, New York, NY, USA, 2008
- Filip Radlinski, Robert Kleinberg, and Thorsten Joachims, Learning Diverse Rankings with Multi-Armed Bandits, in Proceedings of the International Conference on Machine Learning (ICML), 2008
- Filip Radlinski, Madhu Kurup, and Thorsten Joachims, How Does Clickthrough Data Reflect Retrieval Quality?, in Proceedings of the ACM Conference on Information and Knowledge Management (CIKM), ACM, 2008
- Marc Najork, Hugo Zaragoza, and Michael Taylor, HITS on the Web: How does it Compare?, in 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Association for Computing Machinery, Inc., Amsterdam, Netherlands, July 2007
- Michael Taylor, Hugo Zaragoza, Nick Craswell, Stephen Robertson, and Chris Burges, Optimisation methods for ranking functions with multiple parameters, in Proceedings of CIKM 2006, ACM Press, January 2006
- Hugo Zaragoza, Nick Craswell, Michael Taylor, Suchi Saria, and Stephen Robertson, Microsoft Cambridge at TREC 2004: Web and HARD track, in Proceedings of TREC-2004, Gaithersburg, MD: NIST, January 2004
- S. Robertson, H. Zaragoza, and M. Taylor, Simple BM25 extension to multiple weighted fields, in CIKM 2004: Proceedings of the 13th ACM Conference on Information and Knowledge Management, ACM Press, January 2004
- Stephen E. Robertson, Stephen Walker, Hugo Zaragoza, and Ralf Herbrich, Microsoft Cambridge at TREC 2002: Filtering Task, in Proceedings of the Ninth Text Retrieval Conference (TREC-9), January 2002
- Simon Hill, Hugo Zaragoza, Ralf Herbrich, and Peter J. Rayner, Average Precision and the Problem of Generalisation, in Proceedings of the ACM SIGIR Workshop on Mathematical and Formal Methods in Information Retrieval, January 2002
- Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, and Jaz Kandola, The Perceptron Algorithm with Uneven Margins, in Proceedings of the International Conference of Machine Learning, January 2002
- Ralf Herbrich, Thore Graepel, Peter Bollmann–Sdorra, and Klaus Obermayer, Learning a Preference Relation in IR, in Proceedings Workshop Text Categorization and Machine Learning, International Conference on Machine Learning 1998, January 1998
