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Improving the Accuracy of System Performance Estimation by Using Shards

We improve the measurement accuracy of retrieval system performance by better modeling the noise present in test collection scores. Our technique draws its inspiration from two approaches: one, which exploits the variable measurement accuracy of topics; the other, which randomly splits document collections into shards. We describe and theoretically analyze an ANOVA model able to capture the effects of topics, systems, and document shards as well as their interactions. Using multiple TREC collections, we empirically confirm theoretical results in terms of improved estimation accuracy and robustness of found significant differences. The improvements compared to widely used test collection measurement techniques are substantial. We speculate that our technique works because we do not assume that the topics of a test collection measure performance equally.