Retrievability provides an alternative way to assess an Information Retrieval (IR) system by measuring how easily documents can be retrieved. Retrievability can also be used to determine the level of retrieval bias a system exerts upon a collection of documents. It has been hypothesised that reducing the retrieval bias will lead to improved performance. To date, it has been shown that this hypothesis does not appear to hold on standard retrieval performance measures (MAP and P@10) when exploring the parameter space of a given retrieval model. However, the evidence is limited and confined to only a few models, collections and measures. In this paper, we perform a comprehensive empirical evaluation analysing the relationship between retrieval bias and retrieval performance using several well known retrieval models, five large TREC test collections and ten performance measures (including the recently proposed PRES, Time Biased Gain (TBG) and U-Measure). For traditional relevance based measures (MAP, P@10, MRR, Recall, etc) the correlation between retrieval bias and performance is moderate. However, for TBG and U-Measure, we find that there is strong and significant negative correlations between retrieval bias and performance (i.e as bias drops, performance increases). These findings suggest that for these more sophisticated, user oriented measures the retrievability bias hypothesis tends to hold. The implication is that for these measures, systems can then be tuned using retrieval bias, without recourse to relevance judgements.
|Title of host publication||CIKM '14 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management|
|Place of Publication||New York, NY, USA|
|Number of pages||10|
|Publication status||Published - 3 Nov 2014|
- user measures
Wilkie, C., & Azzopardi, L. (2014). A retrievability analysis: exploring the relationship between retrieval bias and retrieval performance. In CIKM '14 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (pp. 81-90). New York, NY, USA. https://doi.org/10.1145/2661829.2661948