BM25
BM25 (Best Matching 25) is a probabilistic ranking function used in information retrieval to score and rank documents based on their relevance to a given search query. It builds upon the TF-IDF (Term Frequency-Inverse Document Frequency) model by incorporating document length normalization and term saturation to improve accuracy, particularly for full-text search in search engines and databases. The algorithm calculates a relevance score for each document by considering term frequency, inverse document frequency, and document length relative to the average document length in the collection.
Developers should learn BM25 when building or optimizing search systems, such as in search engines, recommendation systems, or database queries, as it provides a robust and widely-adopted method for relevance ranking that outperforms simpler models like TF-IDF in many real-world scenarios. It is particularly useful in applications like Elasticsearch, Apache Lucene, and other full-text search tools where handling large document collections with varying lengths and term distributions is critical for delivering accurate search results.