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The performance of a document recommender system is dependent on the quality and characteristics of the query used by the recommender to retrieve relevant documents. Automatically predicting the performance of a query can help identify ineffective queries and can help improve performance by selectively applying query expansion techniques. In this paper, we study Information-entropy-based measures for predicting performance of a query in the context of domain-specific corpora. We propose a newdoi:10.1109/hicss.2007.440 dblp:conf/hicss/SarnikarZZ07 fatcat:ds7bum4arzfspdcea5j73etmgy