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dc.contributor.authorProvost, Foster-
dc.contributor.authorMelville, Prem-
dc.contributor.authorSaar-Tsechansky, Maytal-
dc.identifier.citationProceedings of the Ninth International Conference on Electronic Commerce, August 2007.en
dc.description.abstractElectronic commerce is revolutionizing the way we think about data modeling, by making it possible to integrate the processes of (costly) data acquisition and model induction. The opportunity for improving modeling through costly data acquisition presents itself for a diverse set of electronic commerce modeling tasks, from personalization to customer lifetime value modeling; we illustrate with the running example of choosing offers to display to web-site visitors, which captures important aspects in a familiar setting. Considering data acquisition costs explicitly can allow the building of predictive models at significantly lower costs, and a modeler may be able to improve performance via new sources of information that previously were too expensive to consider. However, existing techniques for integrating modeling and data acquisition cannot deal with the rich environment that electronic commerce presents. We discuss several possible data acquisition settings, the challenges involved in the integration with modeling, and various research areas that may supply parts of an ultimate solution. We also present and demonstrate briefly a unified framework within which one can integrate acquisitions of different types, with any cost structure and any predictive modeling objectiveen
dc.description.sponsorshipNYU, Stern School of Business, IOMS Department, Center for Digital Economy Researchen
dc.format.extent130613 bytes-
dc.titleData acquisition and cost-effective predictive modeling: targeting offers for electronic commerceen
Appears in Collections:CeDER Published Papers

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