Document Score Distribution Models for Query Performance Inference and Prediction

Abstract

Modelling the distribution of document scores returned from an information retrieval (IR) system in response to a query is of both theoretical and practical importance. One of the goals of modelling document scores in this manner is the inference of document relevance. There has been renewed interest of late in modelling document scores using parameterised distributions. Consequently, a number of hypotheses have been proposed to constrain the mixture distribution from which document scores could be drawn. In this article, we show how a standard performance measure (i.e., average precision) can be inferred from a document score distribution using labelled data. We use the accuracy of the inference of average precision as a measure for determining the usefulness of a particular model of document scores. We provide a comprehensive study which shows that certain mixtures of distributions are able to infer average precision more accurately than others. Furthermore, we analyse a number of mixture distributions with regard to the recall-fallout convexity hypothesis and show that the convexity hypothesis is practically useful. Consequently, based on one of the best-performing score-distribution models, we develop some techniques for query-performance prediction (QPP) by automatically estimating the parameters of the document score-distribution model when relevance information is unknown. We present experimental results that outline the benefits of this approach to query-performance prediction.