2002 MiningKnowledgeSharingSitesForViralMarketing

(Richardson & Domingos, 2002) ⇒ Matthew Richardson, and Pedro Domingos. (2002). “Mining Knowledge-Sharing Sites for Viral Marketing.” In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2002). doi:10.1145/775047.775057

Subject Headings: Social Network Mining Algorithm, Viral Marketing

Notes

Cited By

2004

Deepayan Chakrabarti. AutoPart: Parameter-Free Graph Partitioning and Outlier Detection. PKDD, 2004
Angela Y. Wu, Michael Garland, Jiawei Han. Mining scale-free networks using geodesic clustering. KDD, 2004
Deepayan Chakrabarti, Yiping Zhan, Christos Faloutsos. R-MAT: A Recursive Model for Graph Mining. SDM, 2004
Paat Rusmevichientong, Shenghuo Zhu, David Selinger. Identifying early buyers from purchase data. KDD, 2004

Quotes

Abstract

Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mining these networks from data, building probabilistic models of them, and using these models to choose the best viral marketing plan. Knowledge-sharing sites, where customers review products and advise each other, are a fertile source for this type of data mining. In this paper we extend our previous techniques, achieving a large reduction in computational cost, and apply them to data from a knowledge-sharing site. We optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him. We take into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost. Our results show the robustness and utility of our approach."

Mining Knowledge-Sharing Sites

We have chosen to mine Epinions (http://www.epinions.com ), possibly the best known knowledge-sharing site. On Epinions, members submit product reviews, including a rating (from 0 to 5 stars) for any of over one hundred thousand products. As added incentive, reviewers are paid each time one of their reviews is read. Epinions users interact with each other in both of the ways outlined above, by rating reviews, and also by listing reviewers that they trust. The network of trust relationships between users is called the “web of trust”, and is used by Epinions to re-order the product reviews such that a user first sees reviews by users that they trust. The trust relationships between users, and thus the entire web of trust, can be obtained by crawling through the pages of the individual users. With over 75k users and 500k edges in its web of trust, and 586k reviews over 104k products, Epinions is an ideal source for experiments on social networks and viral marketing. Interestingly, we found that the distribution of trust relationships in the web of trust is Zipfian [25], as has been found in many social networks [24]. This is evidence that the web of trust is a representative example of a social network, and thus is a good basis for our study. A Zipfian distribution of trust is also indicative of a skewed distribution of network values, and therefore of the potential utility of viral marketing."

References

[1] A. L. Barabási, R. Albert, and H. Jong. Scale-free characteristics of random networks: The topology of the World Wide Web. Physica A, 281:69-77, 2000.
[2] S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. In: Proceedings of the Seventh International World Wide Web Conference, Brisbane, Australia, 1998. Elsevier.
[3] D. M. Chickering and David Heckerman. A decision theoretic approach to targeted advertising. In: Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence, Stanford, CA, (2000). Morgan Kaufmann
[4] Pedro Domingos and M. Pazanni. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29:103-130, 1997.
[5] Pedro Domingos and Matthew Richardson. Mining the Network Value of Customers. In: Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining, pages 57-66, San Francisco, CA, (2001). ACM Press.
[6] M. Frauenfelder. Revenge of the know-it-alls: Inside the Web’s free-advice revolution. Wired 8(7):144-158, 2000.
[7] K. Gelbrich and R. Nakhaeizadeh. Value Miner: A data mining environment for the calculation of the customer lifetime value with application to the automotive industry. In: Proceedings of the Eleventh European Conference on Machine Learning, pages 154-161, Barcelona, Spain, (2000). Springer.
[8] R. A. Howard. Information value theory. IEEE Transactions on Systems Science and Cybernetics, SSC-2:22-26. 1966
[9] A. M. Hughes. The Complete Database Marketer: Second-Generation Strategies and Techniques for Tapping the Power of you Customer Database. Irwin, Chicago, IL, 1996.
[10] D. Iacobucci, editor. Networks in Marketing. Sage, Thousand Oaks, CA, 1996.
[11] C. L. Isbell, Jr., M. Kearns, D. Korman, S. Singh, and P. Stone. Cobot in LambdaMOO: A social statistics agent. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence, pages 36-41, Austin, TX, (2000). AAAI Press.
[12] D. R. Jackson. Strategic application of customer lifetime value in direct marketing. Journal of Targeting, Measurement and Analysis for Marketing, 1:9-17, 1994.
[13] S. Jurvetson. What exactly is viral marketing? Red Herring, 78:110-112, 2000.
[14] H. Kautz, B. Selman, and M. Shah. ReferralWeb: Combining social networks and collaborative filtering. Communications of the ACM, 40(3):63-66, 1997.
[15] J. M. Kleinberg. Authoritative sources in a hyperlinked environment. In: Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 668-677, Baltimore, MD, (1998). ACM Press.
[16] D. Krackhardt. Structural leverage in marketing. In D. Iacobucci, editor, Networks in Marketing, pages 50-59. Sage, Thousand Oaks, CA, 1996.
[17] R. Kumar, Prabhakar Raghavan, S. Rajagopalan, and A. Tomkins. Extracting large-scale knowledge bases from the Web. In: Proceedings of the Twenty-Fifth International Conference on Very Large Databases, pages 639-650, Edinburgh, Scotland, 1999. Morgan Kaufmann.
[18] C. X. Ling and C. Li. Data mining for direct marketing: Problems and solutions. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pages 73-79, New York, NY, (1998). AAAI Press.
[19] D. R. Mani, J. Drew, A. Betz, and P. Datta. Statistics and data mining techniques for lifetime value modeling. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 94- 103, New York, NY, (1999). ACM Press.
[20] S. Milgram. The small world problem. Psychology Today, 2:60-67, 1967.
[21] L. Page, S. Brin, Rajeev Motwani, and T. Winograd. The PageRank citation ranking: Bringing order to the web. Technical Report, Stanford University, Stanford, CA. 1998
[22] G. Piatetsky-Shapiro and B. Masand. Estimating campaign benefits and modeling lift. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 185-193, San Diego, CA, 1999. ACM Press.
[23] M. F. Schwartz and D. C. M. Wood. Discovering shared interests using graph analysis. Communications of the ACM, 36(8):78-80, 1993.
[24] Stanley Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge, UK, 1994.
[25] G. K. Zipf. Human Behavior and the Principle of Least Effort. Addison-Wesley, Boston, MA, 1949.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2002 MiningKnowledgeSharingSitesForViralMarketing	Pedro Domingos Matthew Richardson			Mining Knowledge-Sharing Sites for Viral Marketing		KDD-2002	http://www.cs.washington.edu/homes/pedrod/papers/kdd02b.pdf			2002