Chapter

Advances in Information Retrieval Theory

Volume 5766 of the series Lecture Notes in Computer Science pp 139-151

Score Distributions in Information Retrieval

  • Avi ArampatzisAffiliated withCarnegie Mellon UniversityUniversity of Amsterdam
  • , Stephen RobertsonAffiliated withCarnegie Mellon UniversityMicrosoft Research
  • , Jaap KampsAffiliated withCarnegie Mellon UniversityUniversity of Amsterdam

* Final gross prices may vary according to local VAT.

Get Access

Abstract

We review the history of modeling score distributions, focusing on the mixture of normal-exponential by investigating the theoretical as well as the empirical evidence supporting its use. We discuss previously suggested conditions which valid binary mixture models should satisfy, such as the Recall-Fallout Convexity Hypothesis, and formulate two new hypotheses considering the component distributions under some limiting conditions of parameter values. From all the mixtures suggested in the past, the current theoretical argument points to the two gamma as the most-likely universal model, with the normal-exponential being a usable approximation. Beyond the theoretical contribution, we provide new experimental evidence showing vector space or geometric models, and BM25, as being “friendly” to the normal-exponential, and that the non-convexity problem that the mixture possesses is practically not severe.