2009 AudienceSelectionforOnLineBrand

• (Provost et al., 2009) ⇒ Foster Provost, Brian Dalessandro, Rod Hook, Xiaohan Zhang, and Alan Murray. (2009). “Audience Selection for on-line Brand Advertising: Privacy-friendly Social Network Targeting.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557098

Subject Headings:

Notes

• Categories and Subject Descriptors: H.2.8 Database Management: Database Applications — Data Mining; I.2.6 Artificial Intelligence: Learning — induction; I.5.1 Pattern Recognition: Models — statistics; J.4 Computer Applications: Social and Behavioral Sciences.
• General Terms: Algorithms, Design, Experimentation

Cited By

• http://scholar.google.com/scholar?q=%22Audience+selection+for+on-line+brand+advertising%3A+privacy-friendly+social+network+targeting%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557098&preflayout=flat#citedby

Quotes

Author Keywords

On-line Advertising, Predictive Modeling, Social Networks, User-generated content, Privacy

Abstract

This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behavior on user-generated content sites, for the purpose of finding good audiences for brand advertising (as opposed to click maximizing, for example). Targeting social-network neighbors resonates well with advertisers, and on-line browsing behavior data counterintuitively can allow the identification of good audiences anonymously. Besides being one of the first papers to our knowledge on data mining for on-line brand advertising, this paper makes several important contributions. We introduce a framework for evaluating brand audiences, in analogy to predictive-modeling holdout evaluation. We introduce methods for extracting quasi-social networks from data on visitations to social networking pages, without collecting any information on the identities of the browsers or the content of the social-network pages. We introduce measures of brand proximity in the network, and show that audiences with high brand proximity indeed show substantially higher brand affinity. Finally, we provide evidence that the quasi-social network embeds a true social network, which along with results from social theory offers one explanation for the increase in brand affinity of the selected audiences.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 AudienceSelectionforOnLineBrand	Foster Provost Alan Murray Brian Dalessandro Rod Hook Xiaohan Zhang			Audience Selection for on-line Brand Advertising: Privacy-friendly Social Network Targeting		KDD-2009 Proceedings		10.1145/1557019.1557098		2009