Information retrieval research involves a range of theoretical and modelling approaches, together with extensive experimentation to develop more accurate, fast and advanced information retrieval and search techniques for a variety of applications.
Our research is centred on statistical models for retrieval and specifically the probabilistic relevance model and the BM25 algorithm.
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.
- University of Cambridge Computer Lab
- City University Department of Information Science
- University College London Department of Computer Science
- Stefano Mizzaro, University of Udine
- Martin Szummer
- Integrated Systems Group
- 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
- 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.
- Information Retrieval
7 J J Thomson Ave
Cambridge CB3 0FB, UK
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