DocumentCode :
244938
Title :
Social Marketing Meets Targeted Customers: A Typical User Selection and Coverage Perspective
Author :
Qi Liu ; Zheng Dong ; Chuanren Liu ; Xing Xie ; Enhong Chen ; Hui Xiong
Author_Institution :
Sch. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China
fYear :
2014
fDate :
14-17 Dec. 2014
Firstpage :
350
Lastpage :
359
Abstract :
The emergence of social networks has provided opportunities for both targeted marketing and viral marketing. By concentrating the efforts on a few key customers, targeted marketing could make the promotion of the items (products) much easier and more cost-effective. On the other hand, viral marketing aims at finding a set of individuals (seeds) to maximize the word-of-mouth propagation of an item. However, these two marketing strategies can only exploit some specific characteristics of the social networks, and the problem of how to combine them together to build a better, stronger business is still open. To that end, in this paper, we propose a general approach for integrated marketing. Specifically, to market a given item, we first generate the item-specific candidate users by a recommendation algorithm, and then select the typical users who have the best balanced utility scores and consumption/social entropy. Next, treating typical users as targeted customers, we study the problem of maximizing information awareness in viral marketing with these constrained targets. Along this line, we define it as a constrained coverage maximization problem, and propose three solutions: GMIC, LMIC and QMIC. Finally, extensive experimental results on real-world datasets demonstrate that our integrated marketing approach could outperform the methods that consider only targeted marketing or viral marketing.
Keywords :
feature selection; marketing data processing; optimisation; recommender systems; social networking (online); GMIC; LMIC; QMIC; constrained coverage maximization problem; recommendation algorithm; social marketing; social network; targeted marketing; user selection; viral marketing; Collaboration; Computational modeling; Cultural differences; Entropy; Greedy algorithms; Linear programming; Social network services; Recommendation; Social Marketing; Targeted Marketing; Viral Marketing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining (ICDM), 2014 IEEE International Conference on
Conference_Location :
Shenzhen
ISSN :
1550-4786
Print_ISBN :
978-1-4799-4303-6
Type :
conf
DOI :
10.1109/ICDM.2014.93
Filename :
7023352
Link To Document :
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