This paper explores a method for more accurately estimating the main effect of the system in a typical test-collection-based evaluation of information retrieval systems, and thus increasing the sensitivity of system comparisons. Randomly partitioning the test document collection allows for multiple tests of a given system and topic (replicates). Bootstrap ANOVA can use these replicates to extract system-topic interactions---something not possible without replicates---yielding a more precise value for the system effect and a narrower confidence interval around that value. Experiments using multiple TREC collections demonstrate that removing the topic-system interactions substantially reduces the confidence intervals around the system effect as well as increases the number of significant pairwise differences found. Further, the method is robust against small changes in the number of partitions used, against variability in the documents that constitute the partitions, and the measure of effectiveness used to quantify system effectiveness.
ACM Transactions on Information Systems
information retrieval, statistical analysis, test collections, topic variance