What are the evaluation metrics used in information retrieval?

There are several evaluation metrics used in information retrieval to assess the effectiveness and performance of retrieval systems. Some of the commonly used metrics include:

1. Precision: Precision measures the proportion of retrieved documents that are relevant to the query. It is calculated as the number of relevant documents retrieved divided by the total number of documents retrieved.

2. Recall: Recall measures the proportion of relevant documents that are retrieved by the system. It is calculated as the number of relevant documents retrieved divided by the total number of relevant documents in the collection.

3. F-measure: F-measure is a combined metric that considers both precision and recall. It is calculated as the harmonic mean of precision and recall, providing a balanced measure of the system's performance.

4. Mean Average Precision (MAP): MAP calculates the average precision across multiple queries. It considers the order in which the documents are retrieved and assigns higher scores to systems that retrieve relevant documents earlier in the ranking.

5. Normalized Discounted Cumulative Gain (NDCG): NDCG measures the quality of the ranking produced by the retrieval system. It takes into account both the relevance of the documents and their position in the ranking, assigning higher scores to systems that retrieve highly relevant documents at the top.

6. Precision at K: Precision at K measures the precision of the top K retrieved documents. It is useful when the user is only interested in the top-ranked results and does not consider the remaining documents.

7. Mean Reciprocal Rank (MRR): MRR calculates the average reciprocal rank of the first relevant document across multiple queries. It provides a measure of how quickly the system retrieves the most relevant document.

These evaluation metrics help researchers and practitioners in assessing and comparing the performance of different retrieval systems, allowing for improvements and optimizations in information retrieval techniques.