Understanding the BM25 Ranking Algorithm | by Everton Gomede, PhD | Medium

In the realm of information retrieval, search engines play a crucial role in assisting users in finding relevant information from vast amounts of data. The efficiency and effectiveness of search engines heavily rely on ranking algorithms. One such algorithm that has gained significant attention and success is the BM25 (Best Match 25) ranking algorithm. BM25 is a term-based ranking model that aims to provide accurate and relevant search results by scoring documents based on their term frequencies and document lengths. This essay explores the fundamental concepts and working principles of the BM25 ranking algorithm. The BM25 algorithm was introduced by Robertson and Walker in 1994 as an improvement over the previous Okapi BM11 algorithm. BM25 takes into account both term frequency (TF) and document length normalization to determine the relevance of a document to a given query. It follows the probabilistic retrieval framework, which assumes that relevant and non-relevant documents follow

In the realm of information retrieval, search engines play a crucial role in assisting users in finding relevant information from vast amounts of data. The efficiency and effectiveness of search engines heavily rely on ranking algorithms. One such algorithm that has gained significant attention and success is the BM25 (Best Match 25) ranking algorithm. BM25 is a term-based ranking model that aims to provide accurate and relevant search results by scoring documents based on their term frequencies and document lengths. This essay explores the fundamental concepts and working principles of the BM