Judging Relevance Using Magnitude Estimation

Abstract

Magnitude estimation is a psychophysical scaling technique whereby numbers are assigned to stimuli to reflect the ratios of their perceived intensity. We report on a crowdsourcing experiment aimed at understanding if magnitude estimation can be used to gather reliable relevance judgements for documents, as is commonly required for test collection-based evaluation of information retrieval systems. Results on a small dataset show that: (i) magnitude estimation can produce relevance rankings that are consistent with more classical ordinal judgements; (ii) both an upper-bounded and an unbounded scale can be used effectively, though with some differences; (iii) the presentation order of the documents being judged has a limited effect, if any; and (iv) only a small number repeat judgements are required to obtain reliable magnitude estimation scores.