Score Aggregation Techniques in Retrieval Experimentation


Sri Devi Ravana
Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia.

Alistair Moffat
Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia.


Status

Proc. Australasian Database Conference, Wellington, New Zealand, January 2009, pages 59-67, Volume 92 of Conferences in Research and Practice in Information Technology.

Abstract

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 geometric mean provides more 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.

Full text