Recall and precision are two essential metrics used to evaluate the performance of search algorithms. Recall measures the ability of a system to find all relevant documents in a dataset, while precision assesses the accuracy of the results returned by the system. Essentially, these two metrics help balance the trade-off between finding as much relevant information as possible and ensuring that the information retrieved is actually useful.
Recall is calculated by dividing the number of relevant documents retrieved by the total number of relevant documents available. For example, if a search system has 100 relevant documents in its database and retrieves 80 of them, the recall is 80%. A high recall is beneficial when the goal is to ensure that users do not miss important information. This is particularly crucial in sensitive contexts, such as legal or medical searches, where missing even a single relevant document could have serious consequences.
On the other hand, precision is calculated by dividing the number of relevant documents retrieved by the total number of documents retrieved. For instance, if the same search system retrieves 100 documents in total, but only 60 of them are relevant, then the precision is 60%. High precision is important when users expect only the most pertinent results without wading through irrelevant information. For instance, in e-commerce, if a user searches for "red shoes," they would prefer to see results that are exclusively red shoes rather than items that are not relevant to their request. Balancing recall and precision is key to creating an effective search system, as focusing too much on one can lead to compromising the other.