Recall as a Measure of Ranking Robustness

Abstract

Researchers use recall to evaluate rankings across a variety of retrieval, recommendation, and machine learning tasks. While there is a colloquial interpretation of recall in set-based evaluation, the research community is far from a principled understanding of recall metrics for rankings. The lack of principled understanding of or motivation for recall has resulted in criticism amongst the retrieval community that recall is useful as a measure at all. In this light, we reflect on the measurement of recall in rankings from a formal perspective. Our analysis is composed of three tenets: recall, robustness, and lexicographic evaluation. First, we formally define 'recall-orientation' as sensitivity to movement of the bottom-ranked relevant item. Second, we analyze our concept of recall orientation from the perspective of robustness with respect to possible searchers and content providers. Finally, we extend this conceptual and theoretical treatment of recall by developing a practical preference-based evaluation method based on lexicographic comparison. Through extensive empirical analysis across 17 TREC tracks, we establish that our new evaluation method, lexirecall, is correlated with existing recall metrics and exhibits substantially higher discriminative power and stability in the presence of missing labels. Our conceptual, theoretical, and empirical analysis substantially deepens our understanding of recall and motivates its adoption through connections to robustness and fairness.