Effective pre-retrieval query performance prediction using similarity and variability evidence

Query performance prediction aims to estimate the quality of answers that a search system will return in response to a particular query. In this paper we propose a new family of pre-retrieval predictors based on information at both the collection and document level. Pre-retrieval predictors are important because they can be calculated from information that is available at indexing time; they are therefore more efficient than predictors that incorporate information obtained from actual search results. Experimental evaluation of our approach shows that the new predictors give more consistent performance than previously proposed pre-retrieval methods across a variety of data types and search tasks.