S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Industrial Engineering (산업공학과) Journal Papers (저널논문_산업공학과)
Semi-Supervised Response Modeling
- Lee, Hyoung-joo; Shin, Hyunjung; Cho, Sungzoon; Hwang, Seong-Seob; MacLachlan, Douglas
- Issue Date
- ELSEVIER SCIENCE INC
- JOURNAL OF INTERACTIVE MARKETING; Vol.24 1; 42-54
- 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.
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