Scalable Inference of Customer Similarities from Interactions Data using Dirichlet Processes
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Braun, Michael; Bonfrer, Andre
Description
Under the sociological theory of homophily, people who are similar to one another are more likely to interact with one another. Marketers often have access to data on interactions among customers from which, with homophily as a guiding principle, inferences could be made about the underlying similarities. However, larger networks face a quadratic explosion in the number of potential interactions that need to be modeled. This scalability problem renders probability models of social interactions...[Show more]
dc.contributor.author | Braun, Michael | |
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dc.contributor.author | Bonfrer, Andre | |
dc.date.accessioned | 2015-12-07T22:27:36Z | |
dc.identifier.issn | 0732-2399 | |
dc.identifier.uri | http://hdl.handle.net/1885/21961 | |
dc.description.abstract | Under the sociological theory of homophily, people who are similar to one another are more likely to interact with one another. Marketers often have access to data on interactions among customers from which, with homophily as a guiding principle, inferences could be made about the underlying similarities. However, larger networks face a quadratic explosion in the number of potential interactions that need to be modeled. This scalability problem renders probability models of social interactions computationally infeasible for all but the smallest networks. In this paper, we develop a probabilistic framework for modeling customer interactions that is both grounded in the theory of homophily and is flexible enough to account for random variation in who interacts with whom. In particular, we present a novel Bayesian nonparametric approach, using Dirichlet processes, to moderate the scalability problems that marketing researchers encounter when working with networked data. We find that this framework is a powerful way to draw insights into latent similarities of customers, and we discuss how marketers can apply these insights to segmentation and targeting activities. | |
dc.publisher | Institute for Operations Research and the Management Sciences (INFORMS) | |
dc.source | Marketing Science: the marketing journal of INFORMS | |
dc.subject | Keywords: Bayesian networks; Dirichlet processes; Homophily; Nonparametric bayes; Probability models; Social networks; Word of mouth | |
dc.title | Scalable Inference of Customer Similarities from Interactions Data using Dirichlet Processes | |
dc.type | Journal article | |
local.description.notes | Imported from ARIES | |
local.identifier.citationvolume | Online | |
dc.date.issued | 2010 | |
local.identifier.absfor | 150505 - Marketing Research Methodology | |
local.identifier.ariespublication | u4602557xPUB19 | |
local.type.status | Published Version | |
local.contributor.affiliation | Braun, Michael, Massachusetts Institute of Technology | |
local.contributor.affiliation | Bonfrer, Andre, College of Business and Economics, ANU | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.startpage | 40 | |
local.identifier.doi | 10.1287/mksc.1110.0640 | |
local.identifier.absseo | 890104 - Mobile Telephone Networks and Services | |
dc.date.updated | 2016-02-24T11:12:58Z | |
local.identifier.scopusID | 2-s2.0-79957629269 | |
Collections | ANU Research Publications |
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