Score Aggregation Techniques in Retrieval Experimentation

Ravana, S.D. and Moffat, A.

    Comparative evaluations of information retrieval systems are based on a number of key premises, including that representative topic sets can be created, that suitable relevance judgements can be generated, and that systems can be sensibly compared based on their aggregate performance over the selected topic set. This paper considers the role of the third of these assumptions - that the performance of a system on a set of topics can be represented by a single overall performance score such as the average, or some other central statistic. In particular, we experiment with score aggregation techniques including the arithmetic mean, the geometric mean, the harmonic mean, and the median. Using past TREC runs we show that an adjusted geometricmean providesmore consistent system rankings than the arithmetic mean when a significant fraction of the individual topic scores are close to zero, and that score standardization (Webber et al., SIGIR 2008) achieves the same outcome in a more consistent manner.
Cite as: Ravana, S.D. and Moffat, A. (2009). Score Aggregation Techniques in Retrieval Experimentation. In Proc. Twentieth Australasian Database Conference (ADC 2009), Wellington, New Zealand. CRPIT, 92. Bouguettaya, A. and Lin, X., Eds. ACS. 59-67.
pdf (from crpit.com) pdf (local if available) BibTeX EndNote GS