Information Retrieval and Data Mining

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

	Retrieval Models Our research is centred on statistical models for retrieval and specifically the probabilistic relevance model and the BM25 algorithm.
	New features Modern retrieval functions must deal with many different predictors of relevance: query matches in different text fields, query independent features, usage data, etc. We seek to incorporate new evidence and design new features which will contribute to better search.
	Optimization and Learning The incorporation of new sources of evidence and features multiplies the number of parameters needed. This in turn makes parameter tuning a crucial step in the development of effective retrieval functions.
	Measurement and Effectiveness Experiments depend on having good methods for measuring the effectiveness of systems. Our research is concerned with understanding the characteristics and behavior of metrics and improving the design of experiments.

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

People

Yilmaz, Emine

Radlinski, Filip

Smyth, Gavin

Szummer, Martin

Taylor, Mike

Shokouhi, Milad

Robertson, Stephen

Vinay, Vishwa