Standard evaluation metrics in information retrieval (IR) include precision, recall, F1 score, Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (nDCG). Precision measures the proportion of retrieved documents that are relevant, while recall evaluates the proportion of relevant documents that are retrieved. F1 score balances these two by calculating the harmonic mean of precision and recall.
MAP and nDCG are more advanced metrics that take the order of results into account. MAP averages precision over all relevant documents for each query, while nDCG gives more weight to documents ranked higher in the search results. Both metrics are particularly useful for tasks like web search, where ranking relevance is critical.
These metrics are essential for evaluating IR systems. For example, in e-commerce, a system with high precision and recall can ensure that customers find relevant products quickly. Evaluating these metrics helps developers refine their models for better search outcomes and user satisfaction.