Recall is the ratio of the positive observations that are actually classified as positives. In binary classification setting, it takes the number of TP and divides it by the sum of TP and FN:
$$ \mathrm{Recall} = \frac{TP}{TP + FN} $$It is also called sensitivity or true positive rate. the correctly classified observations.
While precision uses in the denominator the total number of observations classified as positives, recall uses the total number of observations that are actual positives.
This metric is useful in cases of class imbalance and when the false negative is way riskier than the false positive; a typical task where recall is used as a metric is cancer detection.