Information retrieval (IR) has a large literature on evaluation, dating back decades and forming a central part of the research culture. The largest proportion of this literature discusses techniques to turn a sequence of relevance labels into a single number, reflecting the system’s performance: precision or cumulative gain, for example, or dozens of alternatives. Those techniques—metrics—are themselves evaluated, commonly by reference to sensitivity and validity.<\/p>\n
In our experience measuring search in industrial settings, a measurement regime needs many other qualities to be practical. For example, we must also consider how much a metric costs; how robust it is to the happenstance of sampling; whether it is debuggable; and what activities are incentivised when a metric is taken as a goal.<\/p>\n
In this perspective paper we discuss what makes a search metric successful in large-scale settings, including factors which are not often canvassed in IR research but which are important in “real-world” use. We illustrate this with examples, including from industrial settings, and offer suggestions for metrics as part of a working system.<\/p>\nOpens in a new tab<\/span>","protected":false},"excerpt":{"rendered":" Information retrieval (IR) has a large literature on evaluation, dating back decades and forming a central part of the research culture. The largest proportion of this literature discusses techniques to turn a sequence of relevance labels into a single number, reflecting the system’s performance: precision or cumulative gain, for example, or dozens of alternatives. Those 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