Unsupervised linear score normalization revisited

Ilya Markov, Avi Arampatzis, Fabio Crestani

Abstract: We give a fresh look into score normalization for merging result-lists, isolating the problem from other components. We focus on three of the simplest, practical, and widely-used linear methods which do not require any training data, i.e. MinMax, Sum, and Z-Score. We provide theoretical arguments on why and when the methods work, and evaluate them experimentally. We find that MinMax is the most robust under many circumstances, and that Sum is - in contrast to previous literature - the worst. Based on the insights gained, we propose another three simple methods which work as good or better than the baselines.