Abstract
Social networks provide unparalleled opportunities for marketing products or services. Along this line, tremendous efforts have been devoted to the research of targeted social marketing, where the marketing efforts could be concentrated on a particular set of users with high utilities. Traditionally, these targeted users are identified based on their potential interests to the given company (product). However, social users are usually influenced simultaneously by multiple companies, and not only the user interest but also these social influences will contribute to the user consumption behaviors. To that end, in this paper, we propose a general approach to figure out the targeted users for social marketing, taking both user interests and multiple social influences into consideration. Specifically, we first formulate it as an Identifying Hesitant and Interested Customers (IHIC) problem, where we argue that these valuable users should have the best balanced influence entropy (being “Hesitant”) and utility scores (being “Interested”). Then, we design a novel framework and propose specific algorithms to solve this problem. Finally, extensive experiments on two real-world datasets validate the effectiveness and the efficiency of our proposed approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: WWW, pp. 591–600. ACM (2010)
Hartline, J., Mirrokni, V., Sundararajan, M.: Optimal marketing strategies over social networks. In: WWW, pp. 189–198. ACM (2008)
Liu, L., Yang, Z., Benslimane, Y.: Conducting efficient and cost-effective targeted marketing using data mining techniques. In: GCIS, pp. 102–106. IEEE (2013)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender systems handbook. vol. 1. Springer (2011)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: SIGKDD, pp. 137–146. ACM (2003)
Alowibdi, J.S., Buy, U.A., Yu, P.: Empirical evaluation of profile characteristics for gender classification on twitter. In: ICMLA. vol. 1, pp. 365–369. IEEE (2013)
Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: SIGKDD, pp. 397–406. ACM (2009)
He, X., Song, G., Chen, W., Jiang, Q.: Influence blocking maximization in social networks under the competitive linear threshold model. In: SDM, pp. 463–474. SIAM (2012)
Chen, W., Lakshmanan, L.V., Castillo, C.: Information and influence propagation in social networks. Synthesis Lectures on Data Management 5(4), 1–177 (2013)
Tang, J., Hu, X., Liu, H.: Social recommendation: a review. Social Network Analysis and Mining 3(4), 1113–1133 (2013)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009, 4 (2009)
Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: WSDM, pp. 241–250. ACM (2010)
Kutzkov, K., Bifet, A., Bonchi, F., Gionis, A.: Strip: stream learning of influence probabilities. In: KDD, pp. 275–283 (2013)
Goldenberg, J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12(3), 211–223 (2001)
Granovetter, M.: Threshold models of collective behavior. American Journal of Sociology, 1420–1443 (1978)
Aggarwal, C., Khan, A., Yan, X.: On flow authority discovery in social networks. In: SDM, pp. 522–533 (2011)
Xiang, B., Liu, Q., Chen, E., Xiong, H., Zheng, Y., Yang, Y.: Pagerank with priors: An influence propagation perspective. In: IJCAI, pp. 2740–2746. AAAI Press (2013)
Tan, P.N., Steinbach, M., Kumar, V., et al.: Introduction to data mining. Pearson Addison Wesley, Boston (2006)
Tang, F., Liu, Q., Zhu, H., Chen, E., Zhu, F.: Diversified social influence maximization. In: ASONAM, pp. 455–459. IEEE (2014)
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22(1), 143–177 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ma, G., Liu, Q., Wu, L., Chen, E. (2015). Identifying Hesitant and Interested Customers for Targeted Social Marketing. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9077. Springer, Cham. https://doi.org/10.1007/978-3-319-18038-0_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-18038-0_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18037-3
Online ISBN: 978-3-319-18038-0
eBook Packages: Computer ScienceComputer Science (R0)