Also known as: Recall At K (recall@k) · recall@k · Recall At K recall@k
A metric for evaluating systems that output a ranked (ordered) list of items. Recall at k identifies the fraction of relevant items in the first k items in that list out of the total number of relevant items returned. \\\\\\[\\\\recall at k\ = \\\\\\\\relevant items in first k items of the list\\ \\\\\total number of relevant items in the list\\\\\\\\] Contrast with precision at k. Suppose a large language model is given the following query: And the large language model returns the list shown in the first two columns: Movie --- The General Mean Girls Platoon Bridesmaids This is Spinal Tap Airplane! Groundhog Day Monty Python and the Holy Grail Oppenheimer Clueless Eight of the movies in the preceding list are very funny, so they are "relevant items in the list." Therefore, 8 will be the denominator in all the calculations of recall at k. What about the numerator? Well, 3 of the first 4 items are relevant, so recall at 4 is: 7 of the first 8 movies are very funny, so recall at 8 is: