1. An information retrieval system comprising: an input arranged to receive user query terms; a processor arranged to generate a plurality of lists of documents using the query terms and by accessing a document index; a prediction engine arranged to predict, for each of the lists of documents, implicit relevance information and explicit relevance information; a utility engine arranged to calculate a utility score for each of the lists of documents using the predicted relevance information and a stored utility function; the utility engine also being arranged to select and store one of the lists of documents on the basis of the utility scores.
1. An information retrieval system comprising: an input arranged to receive user query terms; a processor arranged to generate a plurality of lists of documents using the query terms and by accessing a document index; a prediction engine arranged to predict, for each of the lists of documents, implicit relevance information and explicit relevance information; a utility engine arranged to calculate a utility score for each of the lists of documents using the predicted relevance information and a stored utility function; the utility engine also being arranged to select and store one of the lists of documents on the basis of the utility scores. 4. An information retrieval system as claimed in claim 1 wherein the prediction engine is a machine learning system comprising a stored Bayesian generalized linear model comprising a plurality of model weights.