Abstract
We propose a data-mining approach for the targeted marketing of new products that have never been rated or purchased by customers. This approach uncovers associations between customer types and product genres that frequently occurred in previous transaction records. Customer types are defined in terms of demographic attribute values that can be aggregated through concept hierarchies; product types can be generalized through product taxonomies. We use generalized profile-association rules (GP association rules) to identify the advertising targets for a given new product. In addition, we propose two algorithms-GP-Apriori and Merge-prune-to mine GP association rules and develop a value-based targeted advertising algorithm to select prospective customers of a new product on the basis of the discovered rules. We evaluate the proposed approach using both synthetic data and library-circulation data.
| Original language | English |
|---|---|
| Pages (from-to) | 34-45 |
| Number of pages | 12 |
| Journal | INFORMS Journal on Computing |
| Volume | 20 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2008 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Information Systems
- Computer Science Applications
- Management Science and Operations Research
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Discovering generalized profile-association rules for the targeted advertising of new products. / Hwang, San Yih; Yang, Wan Shiou.
In: INFORMS Journal on Computing, Vol. 20, No. 1, 01.01.2008, p. 34-45.Research output: Contribution to journal › Article
TY - JOUR
T1 - Discovering generalized profile-association rules for the targeted advertising of new products
AU - Hwang, San Yih
AU - Yang, Wan Shiou
PY - 2008/1/1
Y1 - 2008/1/1
N2 - We propose a data-mining approach for the targeted marketing of new products that have never been rated or purchased by customers. This approach uncovers associations between customer types and product genres that frequently occurred in previous transaction records. Customer types are defined in terms of demographic attribute values that can be aggregated through concept hierarchies; product types can be generalized through product taxonomies. We use generalized profile-association rules (GP association rules) to identify the advertising targets for a given new product. In addition, we propose two algorithms-GP-Apriori and Merge-prune-to mine GP association rules and develop a value-based targeted advertising algorithm to select prospective customers of a new product on the basis of the discovered rules. We evaluate the proposed approach using both synthetic data and library-circulation data.
AB - We propose a data-mining approach for the targeted marketing of new products that have never been rated or purchased by customers. This approach uncovers associations between customer types and product genres that frequently occurred in previous transaction records. Customer types are defined in terms of demographic attribute values that can be aggregated through concept hierarchies; product types can be generalized through product taxonomies. We use generalized profile-association rules (GP association rules) to identify the advertising targets for a given new product. In addition, we propose two algorithms-GP-Apriori and Merge-prune-to mine GP association rules and develop a value-based targeted advertising algorithm to select prospective customers of a new product on the basis of the discovered rules. We evaluate the proposed approach using both synthetic data and library-circulation data.
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UR - http://www.scopus.com/inward/citedby.url?scp=61349181418&partnerID=8YFLogxK
U2 - 10.1287/ijoc.1060.0210
DO - 10.1287/ijoc.1060.0210
M3 - Article
AN - SCOPUS:61349181418
VL - 20
SP - 34
EP - 45
JO - INFORMS Journal on Computing
JF - INFORMS Journal on Computing
SN - 1091-9856
IS - 1
ER -