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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
People
Szummer, Martin
Szummer, Martin

Taylor, Mike
Taylor, Mike

Robertson, Stephen
Robertson, Stephen

Vinay, Vishwa
Vinay, Vishwa