Information Retrieval

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.
		Evidence 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.

People

• Nick Craswell
• John Guiver
• Ed Snelson
• Michael Taylor
• Stephen Robertson

Collaborators

• University of Cambridge Computer Lab
• City University Department of Information Science
• Stefano Mizzaro, University of Udine
• Martin Szummer
• Integrated Systems Group

Former Members

• Hugo Zaragoza, Yahoo! Research

“The origins of information retrieval are more than half a century old, but search has now become the base technology of the web, that which holds it all together”

— Stephen Robertson, Senior Researcher, Microsoft Research

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• Related Links

• We are looking for strong candidates for internship positions in machine learning, computer vision and information retrieval. These are aimed at students partway through their PhD, and are available throughout the year, not just over the summer.

• Machine Learning and Perception

• Information Retrieval
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