Mean Average Precision (mAP), or Average Precision (AP), is a metric used to evaluate the quality of ranked retrieval results, particularly in tasks like similarity search. AP measures how well a system retrieves relevant items and ranks them higher than non-relevant ones. For a single query, AP calculates the precision (ratio of relevant items retrieved) at each position where a relevant item appears in the ranked list, then averages these values. For example, if a query has three relevant items appearing at positions 1, 3, and 5 in a result list, precision is calculated at each of these positions (1/1=1.0, 2/3≈0.67, 3/5=0.6), and the average of these values (1.0 + 0.67 + 0.6)/3 ≈ 0.76 gives the AP for that query. mAP is the mean of AP values across all queries in a dataset, providing an aggregate measure of retrieval performance.
In similarity search within vector databases, mAP is applied to assess how accurately the database returns relevant vectors (e.g., images, text embeddings) in response to a query. When a query vector is searched against a database, the system ranks results by similarity scores (e.g., cosine similarity). AP evaluates this ranking by checking how early and consistently relevant items appear. For instance, in an image retrieval system, if a query for “red cars” should return 5 relevant images, AP tracks the precision at each position where a relevant image is found. A perfect ranking (all 5 relevant images in the top 5 positions) would yield an AP of 1.0. If relevant items are scattered (e.g., positions 1, 4, 7, 10, 15), AP penalizes the later positions, resulting in a lower score. This makes AP sensitive to both recall (retrieving all relevant items) and ranking quality.
Developers use mAP to benchmark vector databases or machine learning models (e.g., neural networks for embeddings) by comparing their results against ground-truth labels. For example, in a facial recognition system, if 100 queries are tested, each with an average AP score of 0.85, the mAP would be 0.85. This metric is particularly useful because it accounts for varying numbers of relevant items per query and emphasizes the importance of ranking order. A high mAP indicates that the system reliably surfaces relevant results early, which is critical for user-facing applications like search engines or recommendation systems. By optimizing for mAP, developers can iteratively improve their indexing strategies, similarity algorithms, or model training to achieve better retrieval performance.