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Typically, a new model is developed, a learning algorithm is derived, and the approach has to be implemented.<\/jats:p>\n          <jats:p>\n            Factorization machines (FM) are a generic approach since they can mimic most factorization models just by feature engineering. This way, factorization machines combine the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain.\n            <jats:sc>libFM<\/jats:sc>\n            is a software implementation for factorization machines that features stochastic gradient descent (SGD) and alternating least-squares (ALS) optimization, as well as Bayesian inference using Markov Chain Monto Carlo (MCMC). This article summarizes the recent research on factorization machines both in terms of modeling and learning, provides extensions for the ALS and MCMC algorithms, and describes the software tool\n            <jats:sc>libFM<\/jats:sc>\n            .\n          <\/jats:p>","DOI":"10.1145\/2168752.2168771","type":"journal-article","created":{"date-parts":[[2012,10,12]],"date-time":"2012-10-12T20:56:02Z","timestamp":1350075362000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":958,"title":["Factorization Machines with libFM"],"prefix":"10.1145","volume":"3","author":[{"given":"Steffen","family":"Rendle","sequence":"first","affiliation":[{"name":"University of Konstanz"}]}],"member":"320","published-online":{"date-parts":[[2012,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1055709.1055714"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557029"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1639714.1639759"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_2_1_5_1","unstructured":"Chen T. Zheng Z. Lu Q. Zhang W. and Yu Y. 2011. Feature-based matrix factorization. Tech. rep. APEX-TR-2011-07-11 Apex Data & Knowledge Management Lab Shanghai Jiao Tong University. Chen T. Zheng Z. Lu Q. Zhang W. and Yu Y. 2011. Feature-based matrix factorization. Tech. rep. APEX-TR-2011-07-11 Apex Data & Knowledge Management Lab Shanghai Jiao Tong University."},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the NIPS Workshop on Sparse Representation and Low-rank Approximation.","author":"Freudenthaler C."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.129"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Gelman A. Carlin J. B. Stern H. S. and Rubin D. B. 2003. Bayesian Data Analysis 2nd Ed. Chapman and Hall\/CRC. Gelman A. Carlin J. B. Stern H. S. and Rubin D. B. 2003. Bayesian Data Analysis 2nd Ed. 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