# What is “recall” in the context of vector search results, and how is recall typically calculated when evaluating an ANN algorithm against ground-truth neighbors?

Source: https://zilliz.com/ai-faq/what-is-recall-in-the-context-of-vector-search-results-and-how-is-recall-typically-calculated-when-evaluating-an-ann-algorithm-against-groundtruth-neighbors
Author: zilliz

**What is recall in vector search results?** 
Recall in vector search measures how well an Approximate Nearest Neighbor (ANN) algorithm retrieves the true top results compared to an exact (ground-truth) search. When searching high-dimensional data, ANN algorithms trade precision for speed by approximating results, but this can miss some true matches. Recall quantifies the fraction of ground-truth neighbors successfully returned by the ANN method. For example, if an exact search identifies 100 neighbors for a query, and the ANN retrieves 80 of them, the recall is 80%.

**How is recall calculated?** 
Recall is computed by comparing the ANN’s output against a ground-truth dataset generated via brute-force exact search. For a given query, let the ground-truth set contain the top *k* nearest neighbors. If the ANN returns *k* results, recall is the ratio of overlapping items between the ANN’s results and the ground-truth set to *k*. Mathematically, recall = (number of shared items) / *k*. For instance, if the ground-truth has 10 neighbors and the ANN retrieves 7 of them, recall is 0.7. This is repeated across all queries, and the average is reported.

**Practical considerations and examples** 
Ground-truth sets are typically precomputed using exact methods, which are slow but accurate. ANN evaluation often uses fixed *k* (e.g., top 100) for consistency. For example, in image retrieval, if an ANN finds 90 out of 100 true matches for a query image, its recall is 90%. However, if the ANN returns more or fewer results than *k*, the calculation adjusts. If the ANN returns 200 results containing 95 of the ground-truth 100, recall is 95/100 = 0.95. High recall indicates the ANN closely approximates exact results, but it often comes at the cost of higher computational overhead.