{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T18:55:22Z","timestamp":1791399322065,"version":"4.3.3"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2009,6,28]],"date-time":"2009-06-28T00:00:00Z","timestamp":1246147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2009,6,28]]},"DOI":"10.1145\/1557019.1557029","type":"proceedings-article","created":{"date-parts":[[2009,6,30]],"date-time":"2009-06-30T09:10:54Z","timestamp":1246353054000},"page":"19-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":331,"title":["Regression-based latent factor models"],"prefix":"10.1145","author":[{"given":"Deepak","family":"Agarwal","sequence":"first","affiliation":[{"name":"Yahoo! Research, Sunnyvale, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bee-Chung","family":"Chen","sequence":"additional","affiliation":[{"name":"Yahoo! Research, Sunnyvale, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2009,6,28]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"KDD cup and workshop. 2007.  KDD cup and workshop. 2007."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526713"},{"key":"e_1_3_2_2_3_1","volume-title":"NIPS","author":"Agarwal D.","year":"2008","unstructured":"D. Agarwal and B.-C. Chen , Online models for content optimization . In NIPS , 2008 . D. Agarwal and B.-C. Chen, et al. Online models for content optimization. In NIPS, 2008."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281199"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"G. Allenby P. Rossi and R. McCulloch. Hierarchical bayes models: A practitioner's guide. http:\/\/ssrn.com\/abstract=655541 2005.  G. Allenby P. Rossi and R. McCulloch. Hierarchical bayes models: A practitioner's guide. http:\/\/ssrn.com\/abstract=655541 2005.","DOI":"10.2139\/ssrn.655541"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/245108.245124"},{"key":"e_1_3_2_2_7_1","volume-title":"et al. A generalized maximum entropy approach to Bregman co-clustering and matrix approximation. J. of Machine Learning Research","author":"Banerjee A.","year":"2007","unstructured":"A. Banerjee and I. Dhillon , et al. A generalized maximum entropy approach to Bregman co-clustering and matrix approximation. J. of Machine Learning Research , 2007 . A. Banerjee and I. Dhillon, et al. A generalized maximum entropy approach to Bregman co-clustering and matrix approximation. J. of Machine Learning Research, 2007."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281206"},{"key":"e_1_3_2_2_9_1","volume-title":"Soc. B","author":"Booth J.","year":"1999","unstructured":"J. Booth and J. Hobert . Maximizing generalized linear mixed model likelihoods with an automated monte carlo EM algorithm. J.R.Statist . Soc. B , 1999 . J. Booth and J. Hobert. Maximizing generalized linear mixed model likelihoods with an automated monte carlo EM algorithm. J.R.Statist. Soc. B, 1999."},{"key":"e_1_3_2_2_10_1","volume-title":"Recommender Systems Workshop","author":"Claypool M.","year":"1999","unstructured":"M. Claypool and A. Gokhale , et al. Combining content-based and collaborative filters in an online newspaper . In Recommender Systems Workshop , 1999 . M. Claypool and A. Gokhale, et al. Combining content-based and collaborative filters in an online newspaper. In Recommender Systems Workshop, 1999."},{"key":"e_1_3_2_2_11_1","series-title":"Series B","volume-title":"Maximum likelihood from incomplete data via the EM algorithm. J. of the Royal Statistical Society","author":"Dempster A. P.","year":"1977","unstructured":"A. P. Dempster , N. M. Laird , and D. B. Rubin . Maximum likelihood from incomplete data via the EM algorithm. J. of the Royal Statistical Society , Series B , 1977 . A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. J. of the Royal Statistical Society, Series B, 1977."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511790942","volume-title":"Data Analysis using Regression and Multilevel\/Hierarchical Models","author":"Gelman A.","year":"2006","unstructured":"A. Gelman and J. Hill . Data Analysis using Regression and Multilevel\/Hierarchical Models . Cambridge , 2006 . A. Gelman and J. Hill. Data Analysis using Regression and Multilevel\/Hierarchical Models. Cambridge, 2006."},{"key":"e_1_3_2_2_13_1","volume-title":"Annals of Applied Statistics","author":"Gelman A.","year":"2008","unstructured":"A. Gelman and A. Jakulin , et al. A weakly informative default prior distribution for logistic and other regression models . Annals of Applied Statistics , 2008 . A. Gelman and A. Jakulin, et al. A weakly informative default prior distribution for logistic and other regression models. Annals of Applied Statistics, 2008."},{"key":"e_1_3_2_2_14_1","volume-title":"AAAI","author":"Good N.","year":"1999","unstructured":"N. Good and J. B. Schafer , et al. Combining collaborative filtering with personal agents for better recommendations . In AAAI , 1999 . N. Good and J. B. Schafer, et al. Combining collaborative filtering with personal agents for better recommendations. In AAAI, 1999."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/312624.312682"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/312624.312649"},{"key":"e_1_3_2_2_17_1","volume-title":"NIPS","author":"Lee D. L.","year":"2001","unstructured":"D. L. Lee and S. Seung . Algorithms for non-negative matrix factorization . In NIPS , 2001 . D. L. Lee and S. Seung. Algorithms for non-negative matrix factorization. In NIPS, 2001."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4899-3242-6","volume-title":"Generalized Linear Models","author":"McCullagh P.","year":"1989","unstructured":"P. McCullagh and J. A. Nelder . Generalized Linear Models . Chapman &amp;Hall\/CRC, 1989 . P. McCullagh and J. A. Nelder. Generalized Linear Models. Chapman&amp;Hall\/CRC, 1989."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/308574.308679"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150490"},{"key":"e_1_3_2_2_21_1","volume-title":"Gaussian Processes for Machine Learning","author":"Rasmussen C.","year":"2006","unstructured":"C. Rasmussen and C. Williams . Gaussian Processes for Machine Learning . MIT Press , 2006 . C. Rasmussen and C. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102441"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390267"},{"key":"e_1_3_2_2_24_1","volume-title":"NIPS","author":"Salakhutdinov R.","year":"2008","unstructured":"R. Salakhutdinov and A. Mnih . Probabilistic matrix factorization . In NIPS , 2008 . R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, 2008."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/564376.564421"},{"key":"e_1_3_2_2_26_1","volume-title":"AISTATS","author":"Schein A. I.","year":"2003","unstructured":"A. I. Schein , L. K. Saul , and L. H. Ungar . A generalized linear model for principal component analysis of binary data . In AISTATS , 2003 . A. I. Schein, L. K. Saul, and L. H. Ungar. A generalized linear model for principal component analysis of binary data. In AISTATS, 2003."},{"key":"e_1_3_2_2_27_1","volume-title":"Bayesian and Frequentist Approaches to Parametric Predictive Inference","author":"Smith R.","year":"1999","unstructured":"R. Smith . Bayesian and Frequentist Approaches to Parametric Predictive Inference . Oxford University , 1999 . R. Smith. Bayesian and Frequentist Approaches to Parametric Predictive Inference. Oxford University, 1999."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277752"}],"event":{"name":"KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","location":"Paris France","acronym":"KDD09","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","ACM Association for Computing Machinery"]},"container-title":["Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1557019.1557029","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1557019.1557029","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T09:38:39Z","timestamp":1750239519000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1557019.1557029"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,6,28]]},"references-count":28,"alternative-id":["10.1145\/1557019.1557029","10.1145\/1557019"],"URL":"https:\/\/doi.org\/10.1145\/1557019.1557029","relation":{},"subject":[],"published":{"date-parts":[[2009,6,28]]},"assertion":[{"value":"2009-06-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}