隨著網路越來越發達，利用社群網路來散播廣告是商人的行銷手法之一，他們只須選擇小部分的使用者做為接收廣告的對象(在這定義為種子使用者)，而這些人有可能再將廣告資訊傳給他們的朋友，如此一來，不論是一開始廣告商傳送的廣告亦或是朋友傳送的廣告，很多人都能收到這些廣告資訊，達到宣傳的效果。因此，越來越多研究著重於在不超過預算為前提，如何在社群網路上選對這些推銷者能使得對這個產品有高度喜好的人收到資訊的期望值是最大的。然而，這些研究在設定個人喜好的標準時，少考慮使用者的朋友也會影響該使用者對產品的喜好程度，在這篇論文中我們將把這個變因考慮進我們的問題並提出一個有效的演算法，我們的實驗結果顯示這個演算法可以達到良好的效果。

Marketing is convenient, low-cost, and beneficial for small companies to expand their customers through social networks. In the literature, many studies address the influence maximization problem which selects initial users (seeds) to spread the product information such that the number of users receiving the product information is maximized. However, these schemes do not take the social factors (e.g., the beliefs of other persons) into account for predicting the user’s behavioral intention. In this paper, we fill this gap by proposing a new variant of the influence maximization problem (BSS) which asks for a set of seeds with the total cost not greater than a given budget such that the total behavioral intentions of the users influenced is maximized. In addition, we also propose an algorithm for the {BSS} problem. We conduct simulations to evaluate the performance of our algorithm using real traces. Experimental results show that our algorithm outperforms several greedy algorithms.

[1] W. Chen, Y. Wang, and S. Yang, \Efficient influence maximization in social networks," in ACM SIGKDD, 2009.
[2] S. Tang, J. Yuan, X. Mao, X. Y. Li, W. Chen, and G. Dai, \Relationship classification in large scale online social networks and its impact on information propagation," in IEEE INFOCOM, 2011.
[3] P. Domingos and M. Richardson, “Mining the network value of customers,” in
ACM SIGKDD, 2001.
[4] M. Richardson and P. Domingos, “Mining knowledge-sharing sites for viral marketing,” in ACM SIGKDD, 2002.
[5] D. Kempe, J. Kleinberg, and E. Tardos, “Maximizing the spread of influence through a social network,” in ACM SIGKDD, 2003.
[6] Y. Zhang, J. Zhou, and J. Cheng, “Preference-based top-k influential nodes mining in social networks,” in IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 2011.
[7] H. Zhang, T. N. Dinh, and M. T. Thai, “Maximizing the spread of positive influence in online social networks,” in IEEE International Conference on Distributed
Computing Systems, 2013.
[8] L. G. Schiffman and L. L. Kanuk, Consumer Behavior. Prentice HalL, 2003.
28
[9] S. Liu, L. Ying, and S. Shakkottai, “Influence maximization in social networks:
An ising-model-based approach,” in IEEE International Conference on Annual
Allerton, 2010.
[10] H. Kim and E. Yoneki, “Influential neighbours selection for information diffusion in online social networks,” in IEEE International Conference on Computer
Communications and Networks, 2012.
[11] D. T. Nguyen, S. Das, and M. T. Thai, “Influence maximization in multiple online social networks,” in IEEE Globecom, 2013.
[12] S. Khuller, A. Moss, and J. Naor, “The budgeted maximum coverage problem,” in Information Processing Letters, 1999.
[13] Crawdad. [Online]. Available: http://crawdad.cs.dartmouth.edu
[14] Last.fm. [Online]. Available: http://www.last.fm.
[15] C. J. Lin, C. W. Chen, and C. F. Chou, “Preference-aware content dissemination in opportunistic mobile social networks,” in IEEE INFOCOM, 2012.
[16] W. Gao, G. Cao, T. L. Porta, and J. Han, “On exploiting transient social contact patterns for data forwarding in delay-tolerant networks,” in IEEE Transactions on Mobile Computing, 2013.
[17] J. Dawes, “Do data characteristics change according to the number of scale points used? an experiment using 5 point, 7 point and 10 point scales,” in MRS IJMR, 2008.
[18] X. F. Dejun Yang and G. Xue, “Espn: Efficient server placement in probabilistic networks with budget constraint,” INFOCOM'11, pp. 1269 ~1277.