Abstract
In this paper, we propose a data mining framework that utilizes the concept of social network for the targeted advertising of products. This approach discovers the cohesive subgroups from customer's social network which is derived from customer's interaction data. Based on the set of cohesive subgroups, we infer the probabilities of customer s liking a product category from transaction records. Utilizing such information, we construct a targeted advertising system. We evaluate the proposed approach by using real email logs and library-circulation data. The experimental results show that our approach yields better quality of advertisement.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 39th Annual Hawaii International Conference on System Sciences, HICSS'06 |
| DOIs | |
| Publication status | Published - 2006 Oct 17 |
| Event | 39th Annual Hawaii International Conference on System Sciences, HICSS'06 - Kauai, HI, United States Duration: 2006 Jan 4 → 2006 Jan 7 |
Publication series
| Name | Proceedings of the Annual Hawaii International Conference on System Sciences |
|---|---|
| Volume | 6 |
| ISSN (Print) | 1530-1605 |
Other
| Other | 39th Annual Hawaii International Conference on System Sciences, HICSS'06 |
|---|---|
| Country | United States |
| City | Kauai, HI |
| Period | 06-01-04 → 06-01-07 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
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Mining social networks for targeted advertising. / Yang, Wan-Shiou; Cheng, Hung Chi; Dia, Jia Ben; Lin, Hsing Tzu.
Proceedings of the 39th Annual Hawaii International Conference on System Sciences, HICSS'06. 2006. 1579567 (Proceedings of the Annual Hawaii International Conference on System Sciences; Vol. 6).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Mining social networks for targeted advertising
AU - Yang, Wan-Shiou
AU - Cheng, Hung Chi
AU - Dia, Jia Ben
AU - Lin, Hsing Tzu
PY - 2006/10/17
Y1 - 2006/10/17
N2 - In this paper, we propose a data mining framework that utilizes the concept of social network for the targeted advertising of products. This approach discovers the cohesive subgroups from customer's social network which is derived from customer's interaction data. Based on the set of cohesive subgroups, we infer the probabilities of customer s liking a product category from transaction records. Utilizing such information, we construct a targeted advertising system. We evaluate the proposed approach by using real email logs and library-circulation data. The experimental results show that our approach yields better quality of advertisement.
AB - In this paper, we propose a data mining framework that utilizes the concept of social network for the targeted advertising of products. This approach discovers the cohesive subgroups from customer's social network which is derived from customer's interaction data. Based on the set of cohesive subgroups, we infer the probabilities of customer s liking a product category from transaction records. Utilizing such information, we construct a targeted advertising system. We evaluate the proposed approach by using real email logs and library-circulation data. The experimental results show that our approach yields better quality of advertisement.
UR - http://www.scopus.com/inward/record.url?scp=33749620015&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33749620015&partnerID=8YFLogxK
U2 - 10.1109/HICSS.2006.272
DO - 10.1109/HICSS.2006.272
M3 - Conference contribution
SN - 0769525075
SN - 9780769525075
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
BT - Proceedings of the 39th Annual Hawaii International Conference on System Sciences, HICSS'06
ER -