Mean Average Precision (MAP) is a metric used to evaluate the performance of information retrieval (IR) systems, specifically when the results are ranked. MAP measures the average precision at each relevant document rank, then averages these values across all queries in a dataset. Precision is calculated for each query as the number of relevant documents retrieved at various ranks divided by the total number of retrieved documents.
MAP is particularly valuable because it incorporates both the relevance and ranking of documents. For example, if a system ranks relevant documents higher, it will yield a higher MAP score. This makes MAP a useful metric for evaluating systems like web search engines, where the order of results significantly impacts user satisfaction.
Higher MAP scores indicate that the system ranks relevant documents closer to the top of the results. By comparing MAP scores between different algorithms, developers can select the most effective approach for improving retrieval quality and ensuring that users find the most relevant documents first.