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BM25 is a ranking function that ranks a set of documents based on the query terms appearing in each document, regardless of the interrelationship between the query terms within a document (e.g., their relative proximity). It is not a single function, but actually a whole family of scoring functions, with slightly different components and parameters. It is used by search engines to rank matching documents according to their relevance to a given search query and is often referred to as “Okapi BM25,” since the Okapi information retrieval system was the first system implementing this function. The BM25 retrieval formula belongs to the BM family of retrieval models (BM stands for Best Match) that is the weight of a term t in a document d is
Recommended Reading
Robertson SE, Walker S, Beaulieu MM, Gatford M, Payne A. Okapi at trec-4. In: Harman DK, editor. NIST special publication 500-236. Proceedings of the 4th Text Retrieval Conference; 1996.
Robertson SE, Walker S. Some simple approximations to the 2-poisson model for probabilistic weighted retrieval. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; June 1994. p. 232–41.
Robertson SE, Van Rijsbergen CJ, Porter M. Probabilistic models of indexing and searching, Chapter 4. In: Robertson SE, Van Rijsbergen CJ, Williams PW, editors. Information retrieval research. London: Butterworths; 1981. p. 35–56.
Robertson SE, Sparck-Jones K. Relevance weighting of search terms. J Am Soc Inf Sci. 1976;27:129–46.
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Amati, G. (2017). BM25. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_921-2
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DOI: https://doi.org/10.1007/978-1-4899-7993-3_921-2
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