Abstract
In this paper, we propose a framework that utilizes the concept of a social network for the targeted advertising of products. This approach discovers the cohesive subgroups from a customer's social network as derived from the customer's interaction data, and uses them to infer the probability of a customer preferring a product category from transaction records. This information is then used to construct a targeted advertising system. We evaluate the proposed approach by using both synthetic data and real-world data. The experimental results show that our approach does well at recommending relevant products.
| Original language | English |
|---|---|
| Pages (from-to) | 2029-2038 |
| Number of pages | 10 |
| Journal | Expert Systems with Applications |
| Volume | 34 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2008 Apr 1 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
- Computer Science Applications
- Artificial Intelligence
Cite this
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Discovering cohesive subgroups from social networks for targeted advertising. / Yang, Wan-Shiou; Dia, Jia Ben.
In: Expert Systems with Applications, Vol. 34, No. 3, 01.04.2008, p. 2029-2038.Research output: Contribution to journal › Article
TY - JOUR
T1 - Discovering cohesive subgroups from social networks for targeted advertising
AU - Yang, Wan-Shiou
AU - Dia, Jia Ben
PY - 2008/4/1
Y1 - 2008/4/1
N2 - In this paper, we propose a framework that utilizes the concept of a social network for the targeted advertising of products. This approach discovers the cohesive subgroups from a customer's social network as derived from the customer's interaction data, and uses them to infer the probability of a customer preferring a product category from transaction records. This information is then used to construct a targeted advertising system. We evaluate the proposed approach by using both synthetic data and real-world data. The experimental results show that our approach does well at recommending relevant products.
AB - In this paper, we propose a framework that utilizes the concept of a social network for the targeted advertising of products. This approach discovers the cohesive subgroups from a customer's social network as derived from the customer's interaction data, and uses them to infer the probability of a customer preferring a product category from transaction records. This information is then used to construct a targeted advertising system. We evaluate the proposed approach by using both synthetic data and real-world data. The experimental results show that our approach does well at recommending relevant products.
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UR - http://www.scopus.com/inward/citedby.url?scp=37349009076&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2007.02.028
DO - 10.1016/j.eswa.2007.02.028
M3 - Article
AN - SCOPUS:37349009076
VL - 34
SP - 2029
EP - 2038
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 3
ER -