Mining social networks for targeted advertising

Wan Shiou Yang, Hung Chi Cheng, Jia Ben Dia, Hsing Tzu Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

65 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 39th Annual Hawaii International Conference on System Sciences, HICSS'06
Pages137a
DOIs
Publication statusPublished - 2006 Oct 17
Event39th Annual Hawaii International Conference on System Sciences, HICSS'06 - Kauai, HI, United States
Duration: 2006 Jan 42006 Jan 7

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume6
ISSN (Print)1530-1605

Other

Other39th Annual Hawaii International Conference on System Sciences, HICSS'06
CountryUnited States
CityKauai, HI
Period06-01-0406-01-07

Fingerprint

Marketing
Electronic mail
Data mining

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Yang, W. S., Cheng, H. C., Dia, J. B., & Lin, H. T. (2006). Mining social networks for targeted advertising. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences, HICSS'06 (pp. 137a). [1579567] (Proceedings of the Annual Hawaii International Conference on System Sciences; Vol. 6). https://doi.org/10.1109/HICSS.2006.272
Yang, Wan Shiou ; Cheng, Hung Chi ; Dia, Jia Ben ; Lin, Hsing Tzu. / Mining social networks for targeted advertising. Proceedings of the 39th Annual Hawaii International Conference on System Sciences, HICSS'06. 2006. pp. 137a (Proceedings of the Annual Hawaii International Conference on System Sciences).
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Yang, WS, Cheng, HC, Dia, JB & Lin, HT 2006, Mining social networks for targeted advertising. in Proceedings of the 39th Annual Hawaii International Conference on System Sciences, HICSS'06., 1579567, Proceedings of the Annual Hawaii International Conference on System Sciences, vol. 6, pp. 137a, 39th Annual Hawaii International Conference on System Sciences, HICSS'06, Kauai, HI, United States, 06-01-04. https://doi.org/10.1109/HICSS.2006.272

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. p. 137a 1579567 (Proceedings of the Annual Hawaii International Conference on System Sciences; Vol. 6).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.

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Yang WS, Cheng HC, Dia JB, Lin HT. Mining social networks for targeted advertising. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences, HICSS'06. 2006. p. 137a. 1579567. (Proceedings of the Annual Hawaii International Conference on System Sciences). https://doi.org/10.1109/HICSS.2006.272