Mining Social Networks for Targeted Advertising

@inproceedings{citeulike:2759968, 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\&\#146;s social network which is derived from customer\&\#146;s interaction data. Based on the set of cohesive subgroups, we infer the probabilities of customer\&\#146;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.}, author = {Yang, Wan-Shiou and Dia, Jia-Ben and Cheng, Hung-Chi and Lin, Hsing-Tzu }, booktitle = {System Sciences, 2006. HICSS '06. Proceedings of the 39th Annual Hawaii International Conference on}, citeulike-article-id = {2759968}, doi = {10.1109/HICSS.2006.272}, journal = {System Sciences, 2006. HICSS '06. Proceedings of the 39th Annual Hawaii International Conference on}, keywords = {data-mining, social-network}, pages = {137a}, posted-at = {2008-05-06 08:00:07}, priority = {2}, title = {Mining Social Networks for Targeted Advertising}, url = {http://dx.doi.org/10.1109/HICSS.2006.272}, volume = {6}, year = {2006} }

TY - CONF ID - citeulike:2759968 L3 - citeulike-article-id:2759968 TI - Mining Social Networks for Targeted Advertising T2 - System Sciences, 2006. HICSS '06. Proceedings of the 39th Annual Hawaii International Conference on JF - System Sciences, 2006. HICSS '06. Proceedings of the 39th Annual Hawaii International Conference on VL - 6 SP - 137a EP - 137a 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. KW - data-mining KW - social-network AU - Yang, Wan-Shiou AU - Dia, Jia-Ben AU - Cheng, Hung-Chi AU - Lin, Hsing-Tzu PY - 2006/// UR - http://dx.doi.org/10.1109/HICSS.2006.272 ER -