Predicting Query Performance Directly from Score Distributions

The task of predicting query performance has received much attention over the past decade. However, many of the frameworks and approaches to predicting query performance are more heuristic than not. In this paper, we develop a principled framework based on modelling the document score distribution to predict query performance directly. In particular, we (1) show how a standard performance measure (e.g. average precision) can be inferred from a document score distribution. We (2) develop techniques for query performance prediction (QPP) by automatically estimating the parameters of the document score distribution (i.e. mixture model) when relevance information is unknown. Therefore, the QPP approaches developed herein aim to estimate average precision directly. Finally, we (3) provide a detailed analysis of one of the QPP approaches that shows that only two parameters of the five-parameter mixture distribution are of practical importance.