Targeted Advertising Based on Social Network Analysis

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Abstract:

Adverting is one of the most important profit models in internet world. With more than ten years development, internet advertisement becomes smarter than ever and RTB (Real Time Bidding) is becoming the major share in the whole internet advertisement. Under RTB environment, advertisers need more accurate and efficient advertising technology than before. Targeted advertising integrates game theory, big data analysis, data mining and advertising technology and helps to publish advertisement to audiences precisely. Social network is the reflection of real world on internet built on six degrees of separation theory, and it has massive users and enormous access every day and thus collects massive personal information which can help to improve targeted advertising. This paper presents a framework to use social network analysis to improve targeted advertising and introduces clustering and cosine similarity as specified algorithm in the framework.

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1306-1309

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January 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Weihui Dai, Xingyun Dai, Tao Sun. A Smart Targeting System for Online Advertising [J]. Journal of Computers, 2009 (4) 778-786.

Google Scholar

[2] Gerald Tesauro, Rajarshi Das. High-Performance Bidding Agents for the Continuous Double Auction [C]. Proceeding of EC '01 Proceedings of the 3rd ACM conference on Electronic Commerce. New York, ACM, 2001, 206-209.

DOI: 10.1145/501158.501183

Google Scholar

[3] Benjamin Edelman, Michael Ostrovsky, Michael Schwarz. Internet Advertising and the Generalized Second Price Auction: Selling Billions of Dollars Worth of Keywords [J]. The American Economic Review, 1997 (1) 242-259.

DOI: 10.1257/aer.97.1.242

Google Scholar

[4] Brendan Lucier, Renato Paes Leme, Eva Tardos. On Revenue in the Generalized Second Price Auction [C]. WWW'12 Proceedings of the 21st international conference on World Wide Web. New York, ACM, 2012, 361-370.

DOI: 10.1145/2187836.2187886

Google Scholar

[5] Ehud Kalai, Ehud Lehrer. Rational Learning Leads to Nash Equilibrium [J]. Econometrica, 1993 (5) 1019-1045.

DOI: 10.2307/2951492

Google Scholar

[6] Gnesh Iyer, David Soberman. The Targeting of Advertising [J]. Marketing Science, 2005(3) 461-476.

Google Scholar

[7] Danah M Boyd, Nicole B Ellison. Social Network Sites: Definition, History, and Scholarship [J]. Journal of Computer-Mediated Communication, 2007 (13) 210–230.

DOI: 10.1111/j.1083-6101.2007.00393.x

Google Scholar

[8] Mark S Handcock, Adrian E Raftery, Jeremy M Tantrum. Model-based clustering for social network [J]. Journal of the Royal Statistical Society, 2007 (2) 301-354.

DOI: 10.1111/j.1467-985x.2007.00471.x

Google Scholar

[9] Dhiraj Joshi, Daniel Gatica Perez. Discovering Groups of People in Google News [C]. HCM '06 Proceedings of the 1st ACM international workshop on Human-centered multimedia, 2006, 55-64.

DOI: 10.1145/1178745.1178757

Google Scholar

[10] Paola Velardi, Roberto Navigli, Alessandro Cucchiarelli. A New Content-Based Model for Social Network Analysis [C]. Semantic Computing, 2008 IEEE International Conference, 2008, 18-25.

DOI: 10.1109/icsc.2008.30

Google Scholar