BM25

BM25 (Best Matching 25) is a ranking function used in information retrieval to estimate the relevance of documents to a given search query, based on probabilistic principles. It improves upon earlier models like TF-IDF by incorporating document length normalization and term frequency saturation, making it more effective for modern search engines and text-based applications. The function is widely implemented in search libraries and databases to enhance search accuracy and performance.

Also known as: Okapi BM25, BM25F, Best Match 25, BM25 ranking, BM25 algorithm

Why learn BM25?

Developers should learn BM25 when building search systems, such as in e-commerce platforms, document databases, or content management systems, where ranking search results by relevance is critical. It is particularly useful for handling large text datasets, as it provides a robust and tunable method to match queries to documents, outperforming simpler models like TF-IDF in many real-world scenarios. Knowledge of BM25 is essential for optimizing search functionality in applications using tools like Elasticsearch, Apache Lucene, or other IR frameworks.

Learning Resources

• docs: BM25: The Next Generation of TF-IDF
• tutorial: Understanding BM25 - Elasticsearch
• docs: BM25 Algorithm Explained
• course: Information Retrieval: BM25 and Beyond
• tutorial: BM25 in Python with Rank-BM25