Modeling user variance in time-biased gain

Cranfield-style information retrieval evaluation considers variance in user information needs by evaluating retrieval systems over a set of search topics. For each search topic, traditional metrics model all users searching ranked lists in exactly the same manner and thus have zero variance in their per-topic estimate of effectiveness. Metrics that fail to model user variance overestimate the effect size of differences between retrieval systems. The modeling of user variance is critical to understanding the impact of effectiveness differences on the actual user experience. If the variance of a difference is high, the effect on user experience will be low. Time-biased gain is an evaluation metric that models user interaction with ranked lists that are displayed using document surrogates. In this paper, we extend the stochastic simulation of time-biased gain to model the variation between users. We validate this new version of time-biased gain by showing that it produces distributions of gain that agree well with actual distributions produced by real users. With a per-topic variance in its effectiveness measure, time-biased gain allows for the measurement of the effect size of differences, which allows researchers to understand the extent to which predicted performance improvements matter to real users.