Query-performance prediction and cluster ranking: Two sides of the same coin

We show that two tasks which were independently addressed in the information retrieval literature actually amount to the exact same task. The first is query performance prediction; i.e., estimating the effectiveness of a search performed in response to a query in the absence of relevance judgments. The second task is cluster ranking, that is, ranking clusters of similar documents by their presumed effectiveness (i.e., relevance) with respect to the query. Furthermore, we show that several state-of-the-art methods that were independently devised for each of the two tasks are based on the same principles. Finally, we empirically demonstrate that using insights gained in work on query-performance prediction can help, in many cases, to improve the performance of a previously proposed cluster ranking method.