Jure Leskovec
Lada A. Adamic
Abstract
We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective.
- Anderson, C. 2006. The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion. Google Scholar
- Anderson, R. M. and May, R. M. 2002. Infectious Diseases of Humans: Dynamics and Control. Oxford University Press.Google Scholar
- Anonymous. 2005. Profiting from obscurity: What the long tail means for the economics of e-commerce. Economist.Google Scholar
- Bailey, N. 1975. The Mathematical Theory of Infectious Diseases and its Applications. Griffin, London, UK.Google Scholar
- Bass, F. 1969. A new product growth for model consumer durables. Manage. Sci. 15, 5, 215--227.Google Scholar
- Bowman, D. and Narayandas, D. 2001. Managing customerinitiated contacts with manufacturers: The impact on share of category requirements and word-of-mouth behavior. J. Market. Resear. 38, 3 (Aug.), 281--297.Google Scholar
- Bronson, P. 1998. Hotmale. Wired Mag. 6, 12.Google Scholar
- Brown, J. J. and Reingen, P. H. 1987. Social ties and word-of-mouth referral behavior. J. Consum. Resear. 14, 3, 350--362.Google Scholar
- Brynjolfsson, E., Hu, Y., and Smith, M. D. 2003. Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers. Manage. Sci. 49, 11, 1580--1596. Google Scholar
- Burke, K. 2003. As consumer attitudes shift, so must marketing strategies.Google Scholar
- Centola, D. and Macy, M. 2005. Complex contagion and the weakness of long ties. ftp://hive.soc.cornell.edu/mwm14/webpage/WLT.pdf.Google Scholar
- Chevalier, J. and Mayzlin, D. 2006. The effect of word-of-mouth on sales: Online book reviews. J. Market. Resear. 43, 3, 345.Google Scholar
- Chickering, D. M. 2003. Optimal structure identification with greedy search. J. Machine Learn. Resear. 3, 507--554. Google Scholar
- Clauset, A., Newman, M. E. J., and Moore, C. 2004. Finding community structure in very large networks. Physical Rev. E 70, 066111.Google Scholar
- DeBruyn, A. and Lilien, G. 2004. A multi-stage model of word-of-mouth through electronic referrals.Google Scholar
- Erdös, P. and Rényi, A. 1960. On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci. 5, 17--61.Google Scholar
- Frenzen, J. and Nakamoto, K. 1993. Structure, cooperation, and the flow of market information. J. Consum. Resear. 20, 3 (Dec.), 360--375.Google Scholar
- Goldenberg, J., Libai, B., and Muller, E. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Market. Lett. 3, 12, 211--223.Google Scholar
- Gomes, L. 2006. It may be a long time before the long tail is wagging the web. The Wall Street Jounal. July 26 2006.Google Scholar
- Granovetter, M. 1978. Threshold models of collective behavior. Ameri. J. Sociol. 83, 6, 1420--1443.Google Scholar
- Granovetter, M. S. 1973. The strength of weak ties. Ameri. J. Sociol. 78, 1360--1380.Google Scholar
- Gruhl, D., Guha, R., Liben-Nowell, D., and Tomkins, A. 2004. Information diffusion through blogspace. In World Wide Web Conference 2004. Google Scholar
- Hill, S., Provost, F., and Volinsky, C. 2006. Network-based marketing: Identifying likely adopters via consumer networks. Statist. Sci. 21, 2, 256--276.Google Scholar
- Holme, P. and Newman, M. E. J. 2006. Nonequilibrium phase transition in the coevolution of networks and opinions. Physical Rev. E 74, 056108.Google Scholar
- Jurvetson, S. 2000. What exactly is viral marketing? Red Herring 78, 110--112.Google Scholar
- Kempe, D., Kleinberg, J., and Tardos, E. 2003. Maximizing the spread of infuence in a social network. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). Google Scholar
- Killworth, P. and Bernard, H. 1978. Reverse small world experiment. Social Netw. 1, 159--192.Google Scholar
- Leskovec, J., Adamic, L. A., and Huberman, B. A. 2006. The dynamics of viral marketing. In Proceedings of the ACM Conference on Electronic Commerce. 228--237. Google Scholar
- Leskovec, J., Singh, A., and Kleinberg, J. 2006. Patterns of influence in a recommendation network. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Google Scholar
- Linden, G., Smith, B., and York, J. 2003. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7, 1, 76--80. Google Scholar
- Montgomery, A. L. 2001. Applying quantitative marketing techniques to the internet. Interfaces 30, 90--108. Google Scholar
- Resnick, P. and Zeckhauser, R. 2002. Trust among strangers in internet transactions: Empirical analysis of ebays reputation system. In The Economics of the Internet and E-Commerce. Elsevier Science.Google Scholar
- Richardson, M. and Domingos, P. 2002. Mining knowledge-sharing sites for viral marketing. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). Google Scholar
- Rogers, E. M. 1995. Diffusion of Innovations, Fourth ed. Free Press, New York, NY.Google Scholar
- Strang, D. and Soule, S. A. 1998. Diffusion in organizations and social movements: From hybrid corn to poison pills. Ann. Rev. Sociol. 24, 265--290.Google Scholar
- Subramani, M. R. and Rajagopalan, B. 2003. Knowledge-sharing and influence in online social networks via viral marketing. Comm. ACM 46, 12, 300--307. Google Scholar
- Travers, J. and Milgram, S. 1969. An experimental study of the small world problem. Sociometry 32, 425--443.Google Scholar
- Watts, D. 2002. A simple model of global cascades on random networks. In Proceedings of the National Academy of Science 99, 9 (April), 4766--5771.Google Scholar
- Wu, F. and Huberman, B. A. 2004. Social structure and opinion formation. Available at http://ideas.repec.org/p/wpa/wuwpco/0407002.html.Google Scholar
- Yang, S. and Allenby, G. M. 2003. Modeling interdependent consumer preferences. J. Market. Resear. 40, 3 (Aug.), 282--294.Google Scholar
Index Terms
- The dynamics of viral marketing
Recommendations
The dynamics of viral marketing
EC '06: Proceedings of the 7th ACM conference on Electronic commerceWe present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by ...
Spillover Effects in Seeded Word-of-Mouth Marketing Campaigns
Seeded marketing campaigns SMCs involve firms sending products to selected customers and encouraging them to spread word of mouth WOM. Prior research has examined certain aspects of this increasingly popular form of marketing communication, such as ...
Viral Marketing: A Brief Study of Pre-Established Methods and Models for Understanding the Various Implications on the Corporate Sector
The paper explores how viral marketing plays a major role for the companies in the present scenario. This paper gives an insight into the various models to provide an overview into the mathematical aspect associated with viral marketing. At the end the ...
Comments