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A web-based search engine responds to a user's query with a list of documents. This list can be viewed as the engine's model of the user's idea of relevance—the engine ‘believes’ that the first document is the most likely to be relevant, the second is slightly less likely, and so on. We extend this idea to an interactive setting where the system accepts the user's feedback and adjusts its relevance model. We develop three specific models that are integrated as part of a system we call Lighthouse. The models incorporate document clustering and a spring-embedding visualization of inter-document similarity. We show that if a searcher were to use Lighthouse in ways consistent with the model, the expected effectiveness improves—i.e., the relevant documents are found more quickly in comparison to existing methods.