Predicting product adoption in large-scale social networks

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2010, Pages 1039-1048

  Bhatt, Rushi ^a   Chaoji, Vineet ^a   Parekh, Rajesh ^a  

^a YAHOO RESEARCH   (United States)

Author keywords

Behavioral targeting; Online advertising; Predictive modeling; Social network targeting

Indexed keywords

BEHAVIORAL TARGETING; DIRECT MARKETING; INSTANT MESSENGERS; MARKETING CAMPAIGN; OFF-LINE ANALYSIS; ONLINE ADVERTISING; ONLINE SOCIAL NETWORKS; PEER PRESSURE; PREDICTIVE MODELING; PREDICTIVE MODELS; PRODUCT ADOPTION; SOCIAL CONNECTION; SOCIAL INFLUENCE; SOCIAL NETWORKS; USER BEHAVIORS; USER FEATURE;

BEHAVIORAL RESEARCH; FORECASTING; KNOWLEDGE MANAGEMENT; PREDICTIVE CONTROL SYSTEMS; SOCIAL NETWORKING (ONLINE);

MARKETING;

EID: 78651301810     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1871437.1871569     Document Type: Conference Paper

References (32)

1. Nielsen Claritas PRIZM. http://en-us.nielsen.com/tab/product-families/ nielsen-claritas/prizm.
2. RuleQuest Research. Website. http://www.rulequest.com/see5-comparison. html.
3. Nielsen Wire. Website, 2009. http://blog.nielsen.com/nielsenwire/nielsen- news/social-networking-new-global-footprint/.
4. Y.-Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong. Analysis of topological characteristics of huge online social networking services. In WWW '07: Proceedings of the 16th international conference on World Wide Web, pages 835-844, New York, NY, USA, 2007. ACM.
5. A. Anagnostopoulos, R. Kumar, and M. Mahdian. Influence and correlation in social networks. In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 7-15, New York, NY, USA, 2008. ACM.
6. S. Aral, L. Muchnik, and A. Sundararajan. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106(51):21544-21549, December 2009.
7. L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 44-54, New York, NY, USA, 2006. ACM.
8. A. Bagherjeiran and R. Parekh. Combining behavioral and social network data for online advertising. In ICDMW '08: Proceedings of the 2008 IEEE International Conference on Data Mining Workshops, pages 837-846, Washington, DC, USA, 2008. IEEE Computer Society.
9. A.-L. L. Barabási and Z. N. Oltvai. Network biology: understanding the cell's functional organization. Nature reviews. Genetics, 5(2):101-113, February 2004.
10. Y. Chen, D. Pavlov, and J. Canny. Large-scale behavioral targeting. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 209-218. ACM, New York, USA, 2009.
11. C. Chung, J. Koran, L.-j. Lin, and H. Yin. Model for generating user profiles in a behavioral targeting system. U. S. Patent Application, (11/394,374), 2006.
12. M. De Choudhury, W. A. Mason, J. M. Hofman, and D. J. Watts. Inferring relevant social networks from interpersonal communication. In WWW 2010: Proceedings of the 19th international conference on World Wide Web, 2010.
13. P. Domingos and M. Richardson. Mining the network value of customers. In KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 57-66, New York, NY, USA, 2001. ACM.
14. J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189-1232, 1999.
15. M. Gladwell. The tipping point: how little things can make a big difference. Little Brown, Boston, 1st edition, 2000.
16. M. Granovetter. Threshold models of collective behavior. American Journal of Sociology, (83):1420-1443, 1978.
17. J. Hartline, V. Mirrokni, and M. Sundararajan. Optimal marketing strategies over social networks. In WWW '08: Proceeding of the 17th international conference on World Wide Web, pages 189-198, New York, NY, USA, 2008. ACM.
18. S. Hill, F. Provost, and C. Volinsky. Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 22(2):256-275, 2006.
19. D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 137-146, New York, NY, USA, 2003. ACM.
20. D. Kempe, J. Kleinberg, and E. Tardos. Influential nodes in a diffusion model for social networks. In ICALP, 2005.
21. J. Leskovec and E. Horvitz. Planetary-scale views on a large instant-messaging network. In WWW '08: Proceeding of the 17th international conference on World Wide Web, pages 915-924, New York, NY, USA, 2008. ACM.
22. M. Mcpherson, L. S. Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1):415-444, 2001.
23. J. Pine. Mass customizing products and services. Planning Review, July-August, 1993.
24. F. Provost, B. Dalessandro, R. Hook, X. Zhang, and A. Murray. Audience selection for on-line brand advertising: privacy-friendly social network targeting. In KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 707-716, New York, NY, USA, 2009. ACM.
25. M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In KDD '02: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 51-70, New York, NY, USA, 2002. ACM.
26. Y. Richter, E. Yom-Tov, and N. Slonim. Predicting customer churn in mobile networks through analysis of social groups. In SDM, pages 732-741, 2010.
27. P. Singla and M. Richardson. Yes, there is a correlation: - from social networks to personal behavior on the web. In WWW '08: Proceeding of the 17th international conference on World Wide Web, pages 655-664, New York, NY, USA, 2008. ACM.
28. R. Walkeri. The hidden (in plain sight) persuaders. The New York Times Magazine, pages 69-75, Dec. 5, 2004.
29. D. J. Watts. A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences of the United States of America, 99(9):5766-5771, April 2002.
30. D. J. Watts and P. S. Dodds. Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4):441-458, 2007.
31. D. J. Watts and S. H. Strogatz. Collective dynamics of 'small-world' networks. Nature, 393:440-442, June 1998.
32. J. Yan, N. Liu, G. Wang, W. Zhang, Y. Jiang, and Z. Chen. How much can behavioral targeting help online advertising? In Proceedings of the 18th International World Wide Web Conference (WWW 2009), pages 261-270, 2009.