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Semi-Supervised Response Modeling
DC Field | Value | Language |
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dc.contributor.author | Lee, Hyoung-joo | - |
dc.contributor.author | Shin, Hyunjung | - |
dc.contributor.author | Cho, Sungzoon | - |
dc.contributor.author | Hwang, Seong-Seob | - |
dc.contributor.author | MacLachlan, Douglas | - |
dc.date.accessioned | 2011-12-02T06:04:33Z | - |
dc.date.available | 2011-12-02T06:04:33Z | - |
dc.date.issued | 2010-02 | - |
dc.identifier.citation | JOURNAL OF INTERACTIVE MARKETING; Vol.24 1; 42-54 | - |
dc.identifier.issn | 1094-9968 | - |
dc.identifier.uri | https://hdl.handle.net/10371/75006 | - |
dc.description.abstract | Response modeling is concerned with identifying potential customers who are likely to purchase a promoted product, based on customers'''''''' demographic and behavioral data. Constructing a response model requires a preliminary campaign result database. Customers who responded to the campaign are labeled as respondents while those who did not are labeled as non-respondents. Those customers who were not chosen for the preliminary campaign do not have labels, and thus are called unlabeled. Then, using only those labeled customer data, a classification model is built in the supervised learning framework to predict all existing customers. However, often in response modeling, only a small part of customers are labeled, and thus available for model building, while a large number of unlabeled data may give valuable information. As a method to exploit the unlabeled data, we introduce semi-supervised learning to the interactive marketing community. A case study on the CoIL Challenge 2000 and the Direct Marketing Educational Foundation data sets shows that the transductive support vector machine, one of widely used semi-supervised models, can identify more respondents than conventional supervised models, especially when a small number of data are labeled. Semi-supervised learning is a viable alternative and merits further investigation. (C) 2009 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All fights reserved. | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.subject | Scoring model | - |
dc.subject | Response modeling | - |
dc.subject | Classification | - |
dc.subject | Semi-supervised learning | - |
dc.title | Semi-Supervised Response Modeling | - |
dc.type | Article | - |
dc.contributor.AlternativeAuthor | 이형주 | - |
dc.contributor.AlternativeAuthor | 신현정 | - |
dc.contributor.AlternativeAuthor | 조성준 | - |
dc.contributor.AlternativeAuthor | 황성섭 | - |
dc.identifier.doi | 10.1016/j.intmar.2009.10.004 | - |
dc.citation.journaltitle | JOURNAL OF INTERACTIVE MARKETING | - |
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dc.description.tc | 1 | - |
dc.identifier.wosid | 000274674700005 | - |
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