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Semi-Supervised Response Modeling
Cited 14 time in
Web of Science
Cited 16 time in Scopus
- Authors
- Issue Date
- 2010-02
- Publisher
- ELSEVIER SCIENCE INC
- Citation
- JOURNAL OF INTERACTIVE MARKETING; Vol.24 1; 42-54
- Keywords
- Scoring model ; Response modeling ; Classification ; Semi-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
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