Combining Propensity and Influence Models for Product Adoption Prediction
Pages 49 - 56
Abstract
This paper studies the problem of selecting users in an online social network for targeted advertising so as to maximize the adoption of a given product. In previous work, two families of models have been considered to address this problem: direct targeting and network-based targeting. The former approach targets users with the highest propensity to adopt the product, while the latter approach targets users with the highest influence potential -- that is users whose adoption is most likely to be followed by subsequent adoptions by peers. This paper proposes a hybrid approach that combines a notion of propensity and a notion of influence into a single utility function. We show that targeting a fixed number of high-utility users results in more adoptions than targeting either highly influential users or users with high propensity.
References
[1]
G. Lantos, Consumer Behavior in Action: Real-Life Applications for Marketing Managers. M. E. Sharpe Incorporated, 2010. {Online}. Available: http://books.google.ee/books?id=JemkYebV5NYC
[2]
P. Domingos and M. Richardson, "Mining the network value of customers," in Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2001, pp. 57--66.
[3]
M. Richardson and P. Domingos, "Mining knowledge-sharing sites for viral marketing," in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2002, pp. 61--70.
[4]
R. Bhatt, V. Chaoji, and R. Parekh, "Predicting product adoption in large-scale social networks," ACM Conference on Knowledge Discovery and Data Mining, pp. 1039--1048, 2010.
[5]
S. Hill, F. Provost, and C. Volinsky, "Network-based marketing: Identifying likely adopters via consumer networks," Statistical Science, pp. 256--276, 2006.
[6]
M. Cha, H. Haddadi, F. Benevenuto, and K. Gummadi, "Measuring user influence in twitter: The million follower fallacy," Proceedings of International AAAI Conference on Weblogs and Social Media (ICWSM), 2010.
[7]
P. R. Sundsøy, J. Bjelland, G. Canright, K. Engø-Monsen, and R. Ling, "Product adoption networks and their growth in a large mobile phone network," in Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on. IEEE, 2010, pp. 208-- 216.
[8]
K. Liu and L. Tang, "Large-scale behavioral targeting with a social twist," in Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, 2011, pp. 1815--1824.
[9]
M. Cha, A. Mislove, and K. P. Gummadi, "A measurement-driven analysis of information propagation in the flickr social network," in Proceedings of the 18th international conference on World wide web. ACM, 2009, pp. 721--730.
[10]
Y. Yu, T. Y. Berger-Wolf, J. Saia et al., "Finding spread blockers in dynamic networks," in Advances in Social Network Mining and Analysis. Springer, 2010, pp. 55--76.
[11]
H. Habiba, "Critical individuals in dynamic population networks," Ph.D. dissertation, Northwestern University, 2013.
[12]
D. M. Luu, E. P. Lim, T. A. Hoang, and F. C. Chua, "Modeling diffusion in social networks using network properties." in International AAAI Conference on Weblogs and Social Media, 2012.
[13]
P. V. Marsden and K. E. Campbell, "Measuring tie strength," Social forces, vol. 63, no. 2, pp. 482--501, 1984.
[14]
J. Bughin, J. Doogan, and O. J. Vetvik, "A new way to measure word-of-mouth marketing," McKinsey Quarterly, vol. 2, pp. 113--116, 2010.
[15]
S. Aral and D. Walker, "Identifying influential and susceptible members of social networks," Science, vol. 337, no. 6092, pp. 337--341, 2012.
[16]
S. A. Thompson and R. K. Sinha, "Brand communities and new product adoption: The influence and limits of oppositional loyalty," Journal of marketing, vol. 72, no. 6, pp. 65--80, 2008.
[17]
P. J. Bateman, P. H. Gray, and B. S. Butler, "Research note -- the impact of community commitment on participation in online communities," Information Systems Research, vol. 22, no. 4, pp. 841--854, 2011.
[18]
J. Preece and B. Shneiderman, "The reader-to-leader framework: Motivating technology-mediated social participation," AIS Transactions on Human-Computer Interaction, vol. 1, no. 1, pp. 13--32, 2009.
[19]
G. Oestreicher-Singer and G. Zalmanson, "Paying for content or paying for community? the effect of social computing platforms on willingness to pay in content websites," Working paper, Tel-Aviv University, Tech. Rep., 2011.
[20]
P. A. Dow, L. A. Adamic, and A. Friggeri, "The anatomy of large facebook cascades," in Proceedings of the 7th International AAAI Conference on Weblogs and Social Media, ICWSM, 2013.
[21]
J. Leskovec, A. Singh, and J. Kleinberg, "Patterns of influence in a recommendation network," in Advances in Knowledge Discovery and Data Mining. Springer, 2006, pp. 380--389.
[22]
G. Sharad, D. J. Watts, and D. G. Goldstein, "The structure of online diffusion networks," Proceedings of the 13th ACM Conference on Electronic Commerce, pp. 623--638, 2012.
[23]
A. Goyal, F. Bonchi, and L. V. Lakshmanan, "Learning influence probabilities in social networks," in Proceedings of the 3rd ACM international conference on Web search and data mining. ACM, 2010, pp. 241--250.
[24]
K. Dave, R. Bhatt, and V. Varma, "Modelling action cascades in social networks," in International AAAI Conference on Weblogs and Social Media, 2011. {Online}. Available: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2741
[25]
W. Chen, Y. Wang, and S. Yang, "Efficient influence maximization in social networks," in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009, pp. 199--208.
[26]
S. Wasserman, Social network analysis: Methods and applications. Cambridge university press, 1994, vol. 8.
[27]
J.-P. Onnela and F. Reed-Tsochas, "Spontaneous emergence of social influence in online systems," Proceedings of the National Academy of Sciences, vol. 107, no. 43, pp. 18 375--18 380, 2010.
[28]
R. Iyengar, C. Van den Bulte, and T. W. Valente, "Opinion leadership and social contagion in new product diffusion," Marketing Science, vol. 30, no. 2, pp. 195--212, 2011.
[29]
O. Hinz, C. Schulze, and C. Takac, "New product adoption in social networks: Why direction matters," Journal of Business Research, vol. 67, no. 1, pp. 2836--2844, 2014.
[30]
D. J. Watts and P. S. Dodds, "Influentials, networks, and public opinion formation," Journal of consumer research, vol. 34, no. 4, pp. 441--458, 2007.
[31]
J. P. Davin, S. Gupta, and M. J. Piskorski, "Separating homophily and peer influence with latent space," Available at SSRN 2373273, 2013.
[32]
C. R. Shalizi and A. C. Thomas, "Homophily and contagion are generically confounded in observational social network studies," Sociological methods & research, vol. 40, no. 2, pp. 211--239, 2011.
- Combining Propensity and Influence Models for Product Adoption Prediction
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Published: 25 August 2015
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