Abstract
Conference Information: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningParis, FRANCE, JUN 28-JUL 01, 2009ACM SIGKDD; ACM SIGMOD Abstract: This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behaviour 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 neighbours resonates well with advertisers, and on-line browsing behaviour data counter intuitively 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.
Information about authors
Author(s): Provost F (Provost, Foster)1, Dalessandro B (Dalessandro, Brian)1, Hook R (Hook, Rod), Zhang XH (Zhang, Xiaohan)1, Murray A (Murray, Alan) Book Group Author(s): ACM Source: KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING Pages: 707-715 Published: 2009
Conference Information: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Paris, FRANCE, JUN 28-JUL 01, 2009
ACM SIGKDD; ACM SIGMOD
Click the following link for further details.
http://pages.stern.nyu.edu/~fprovost/Papers/kdd_audience.pdf
Conference Information: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningParis, FRANCE, JUN 28-JUL 01, 2009ACM SIGKDD; ACM SIGMOD Abstract: This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behaviour 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 neighbours resonates well with advertisers, and on-line browsing behaviour data counter intuitively 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.
Information about authors
Author(s): Provost F (Provost, Foster)1, Dalessandro B (Dalessandro, Brian)1, Hook R (Hook, Rod), Zhang XH (Zhang, Xiaohan)1, Murray A (Murray, Alan) Book Group Author(s): ACM Source: KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING Pages: 707-715 Published: 2009
Conference Information: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Paris, FRANCE, JUN 28-JUL 01, 2009
ACM SIGKDD; ACM SIGMOD
Click the following link for further details.
http://pages.stern.nyu.edu/~fprovost/Papers/kdd_audience.pdf
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