Predicting Query Performance by Query-Drift Estimation

Predicting query performance, that is, the effectiveness of a search performed in response to a query, is a highly important and challenging problem. We present a novel approach to this task that is based on measuring the standard deviation of retrieval scores in the result list of the documents most highly ranked. We argue that for retrieval methods that are based on document-query surface-level similarities, the standard deviation can serve as a surrogate for estimating the presumed amount of query drift in the result list, that is, the presence (and dominance) of aspects or topics not related to the query in documents in the list. Empirical evaluation demonstrates the prediction effectiveness of our approach for several retrieval models. Specifically, the prediction quality often transcends that of current state-of-the-art prediction methods.