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…) to improve the creation of ranking models for information retrieval. Common applications are in search engines, collaborative filtering, machine translation, biological computation, etc. The idea was introduced in 1992 by Norbert Fuhr, describing learning in information retrieval as a parameter estimation problem.
,d 2 ,...,d N } IR System Query q m+1 List of documents (ranked) d q,1 , f (qm+1, d1) d q,2, f (qm+1, d1) d q,3, f (qm+1, d1) d q,4, f (qm+1, d1) d q,5, f (qm+1, d1) ... d q,n, f (qm+1, d1) Learning System q 1 d 1,1 d 1,2 d 1,3 ... d q,n q m d m,1 d m,2 d m,3 ... d m,n f (q,d )
groups: • Pointwise: If we assume that each pair (query, document) get a score, then the problem can be approximated by a regression. • Pairwise: In this case the problem is treated as a classification problem, learning how to better classify each given pair of documents. • Listwise: The last case try to optimize the value of one of previous methods, averaged overall queries. Order of quality: Listwise > Pairwise > Pointwise.
LamdaMart by Chris C.J Burges et others. www.microsoft.com/en-us/research/publication/ranking-boosting-and- model-adaptation/?from=http%3A%2F%2Fresearch.microsoft.com%2F pubs%2F69536%2Ftr-2008-109.pdf • RankSVM or (*) Gradient descendant variants.
Lamere.The Million Song Dataset. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), 2011. Million Song Dataset, official website by Thierry Bertin-Mahieux, available at: http://labrosa.ee.columbia.edu/millionsong/ Tie-Yan Liu (2009), "Learning to Rank for Information Retrieval", Foundations and Trends in Information Retrieval, Foundations and Trends in Information Retrieval, 3 (3): 225–331, doi:10.1561/1500000016, ISBN 978-1-60198-244-5.