Name
Abstract | ||
|---|---|---|
Targeted marketing typically involves the identification of customers or products having potential market values. We propose a linear model for solving this problem by drawing and extending results from information retrieval. It is assumed that each object is represented by values of a finite set of attributes. A market value function, which is a linear combination of utility functions on attribute values, is used to rank objects. Several methods are examined for mining market value functions. The main advantage of the model is that one can rank objects of interest according to their market values, instead of classifying the objects. Both the theoretical and experimental results are reported in this paper. It establishes a basis on which further studies and experimental evaluation can be carried out. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1142/S0218001402002180 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
data mining, targeted marketing, e-commerce, market value functions, business intelligence | Linear combination,Data mining,Finite set,Linear model,Bellman equation,Information extraction,Artificial intelligence,Business intelligence,Market value,Machine learning,Mathematics,E-commerce | Journal |
Volume | Issue | ISSN |
16 | 8 | 0218-0014 |
Citations | PageRank | References |
19 | 1.35 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Y. Yao | 1 | 9707 | 674.28 |
| Ning Zhong | 2 | 2907 | 300.63 |
| Jiajin Huang | 3 | 69 | 15.70 |
| Chuangxin Ou | 4 | 35 | 3.06 |
| Chunnian Liu | 5 | 561 | 61.58 |