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Semi-Supervised Response Modeling

Cited 10 time in Web of Science Cited 13 time in Scopus
Authors
Lee, Hyoung-joo; Shin, Hyunjung; Cho, Sungzoon; Hwang, Seong-Seob; MacLachlan, Douglas
Issue Date
2010-02
Publisher
ELSEVIER SCIENCE INC
Citation
JOURNAL OF INTERACTIVE MARKETING; Vol.24 1; 42-54
Keywords
Scoring modelResponse modelingClassificationSemi-supervised learning
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.
ISSN
1094-9968
Language
English
URI
https://hdl.handle.net/10371/75006
DOI
https://doi.org/10.1016/j.intmar.2009.10.004
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Journal Papers (저널논문_산업공학과)
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