Optimizing ranking functions: a connectionist approach to adaptive information retrieval
Optimizing ranking functions: a connectionist approach to adaptive information retrieval
An introduction to genetic algorithms
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SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
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Many current retrieval models and scoring functions contain free parameters which need to be set--ideally, optimized. The process of optimization normally involves some training corpus of the usual document-query-relevance judgement type, and some choice of measure that is to be optimized. The paper proposes a way to think about the process of exploring the space of parameter values, and how moving around in this space might be expected to affect different measures. One result, concerning local optima, is demonstrated for a range of rank-based evaluation measures.