Modelling A User Population for Designing Information Retrieval Metrics.
Tetsuya Sakai, Stephen E. Robertson · 2008
Although Average Precision (AP) has been the most widely-used retrieval effectiveness metric since the advent of Text Retrieval Conference (TREC), the general belief among researchers is that it lacks a user model. In light of this, Robertson recently pointed out that AP can be interpreted as a special case of Normalised Cumulative Precision (NCP), computed as an expectation of precision over a population of users who eventually stop at different ranks in a list of retrieved documents. He regards AP as a crude version of NCP, in that the probability distribution of the user’s stopping behaviour is uniform across all relevant documents. In this paper, we generalise NCP further and demonstrate that AP and its graded-relevance version Q-measure are in fact reasonable metrics despite the above uniform probability assumption. From a probabilistic perspective, these metrics emphasise long-tail users who tend to dig deep into the ranked list, and thereby achieve high reliability. We also demonstrate that one of our new metrics, called NCU gu,β=1, maintains high correlation with AP and shows the highest discriminative power, i.e., the proportion of statistically significantly different system pairs given a confidence level, by utilising graded relevance in a novel way. Our experimental results are consistent across NTCIR and TREC.