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Published in:
2011 | OriginalPaper | Chapter
Measuring the Ability of Score Distributions to Model Relevance
Author : Ronan Cummins
Published in: Information Retrieval Technology
Publisher: Springer Berlin Heidelberg
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Modelling the score distribution of documents returned from any information retrieval (IR) system is of both theoretical and practical importance. The goal of which is to be able to infer relevant and non-relevant documents based on their score to some degree of confidence.
In this paper, we show how the performance of mixtures of score distributions can be compared using inference of query performance as a measure of
utility
. We (1) outline methods which can directly calculate average precision from the parameters of a mixture distribution. We (2) empirically evaluate a number of mixtures for the task of inferring query performance, and show that the log-normal mixture can model more relevance information compared to other possible mixtures. Finally, (3) we perform an empirical analysis of the mixtures using the recall-fallout convexity hypothesis.