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May 9, Wednesday
13:00 – 14:00

We present a novel framework for the query-performance prediction task. That is, estimating the effectiveness of a search performed by a search engine in response to a query in lack of relevance judgments. The framework is based on estimating the utility that a given document ranking provides with respect to an information need expressed by the query. To address the uncertainty in inferring the information need, we estimate the utility by the expected similarity between the given ranking and those induced by relevance language models. Specific query-performance predictors instantiated from the framework are shown to substantially outperform state-of-the-art predictors. In addition, we present an extension of the framework that results in a unified prediction model that can be used to derive and/or explain several previously proposed post-retrieval predictors which are presumably based on different principles.