14. A computer-implemented method of retrieving a ranked list of documents from an index of documents comprising receiving at least one query term; generating a plurality of lists of documents from the index using the query term; predicting, for each of the lists of documents, implicit relevance information and explicit relevance information; calculating a utility score for each of the lists of documents using the predicted relevance information and a stored utility function; selecting and storing one of the lists of documents on the basis of the utility scores.
14. A computer-implemented method of retrieving a ranked list of documents from an index of documents comprising receiving at least one query term; generating a plurality of lists of documents from the index using the query term; predicting, for each of the lists of documents, implicit relevance information and explicit relevance information; calculating a utility score for each of the lists of documents using the predicted relevance information and a stored utility function; selecting and storing one of the lists of documents on the basis of the utility scores. 15. A computer-implemented method as claimed in claim 14 which comprises using a machine learning system comprising a stored probabilistic model of the form p(y|x,a) to make the prediction by applying the model to give the probability p of observing y after selecting a when x is observed, where the symbol x represents inputs to the prediction engine, the symbol y represents outputs from the model being predicted relevance information, and the symbol a represents a list of documents.