Audience selection for on-line brand advertising
- 28 June 2009
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 707-716
- https://doi.org/10.1145/1557019.1557098
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.Keywords
This publication has 16 references indexed in Scilit:
- A brief survey on anonymization techniques for privacy preserving publishing of social network dataACM SIGKDD Explorations Newsletter, 2008
- Learning classifiers from only positive and unlabeled dataPublished by Association for Computing Machinery (ACM) ,2008
- The future of advertising and the value of social network websitesPublished by Association for Computing Machinery (ACM) ,2007
- Measuring and extracting proximity in networksPublished by Association for Computing Machinery (ACM) ,2006
- Network-based marketing: Identifying likely adopters via consumer networksStatistical Science, 2006
- Neighborhood Formation and Anomaly Detection in Bipartite GraphsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- The myth of the double-blind review?ACM SIGKDD Explorations Newsletter, 2003
- Algorithms for estimating relative importance in networksPublished by Association for Computing Machinery (ACM) ,2003
- SimRankPublished by Association for Computing Machinery (ACM) ,2002
- Birds of a Feather: Homophily in Social NetworksAnnual Review of Sociology, 2001