Scalable inference of customer similarities from interactions data using Dirichlet processes Braun, Michael and Bonfrer, André 2011, Scalable inference of customer similarities from interactions data using Dirichlet processes, Marketing science, vol. 30, no. 3, pp. 513-531, doi: 10.1287/mksc.1110.0640. Attached Files Name Description MIMEType Size Downloads Title Scalable inference of customer similarities from interactions data using Dirichlet processes Author(s) Braun, Michael Bonfrer, André orcid.org/0000-0002-3061-7103 Journal name Marketing science Volume number 30 Issue number 3 Start page 513 End page 531 Total pages 19 Publisher The Institute of Management Sciences and the Operations Research Society of America Place of publication [Providence, R.I.] Publication date 2011-05 ISSN 0732-2399 1526-548X Keyword(s) social networks nonparametric Bayes Dirichlet processes word of mouth homophily probability models Bayesian networks Language eng DOI 10.1287/mksc.1110.0640 Field of Research 1505 Marketing HERDC Research category C1.1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2011, INFORMS Persistent URL http://hdl.handle.net/10536/DRO/DU:30108288 Document type: Journal Article Collections: Faculty of Business and Law Department of Marketing Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 11 May 2018, 15:29:13 EST Fri, 11 May 2018, 15:29:46 EST Sat, 12 May 2018, 05:00:51 EST Mon, 14 May 2018, 08:16:04 EST Wed, 23 May 2018, 06:03:54 EST Wed, 23 May 2018, 10:47:37 EST Wed, 23 May 2018, 10:50:51 EST Wed, 23 May 2018, 10:51:46 EST Filtered Full Citation counts:   Cited 22 times in TR Web of Science Cited 23 times in Scopus Search Google Scholar Access Statistics: 218 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Fri, 11 May 2018, 15:29:46 EST

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