Audience selection for on-line brand advertising: privacy-friendly social network targeting

By Foster J. Provost, Brian M. D’Alessandro, Rod Hook, Xiaohan Zhang, Alan Murray. 2009

152 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals (2016, background): “A particularly ironic instance of the latter problem comes with the use of ‘‘doubly anonymized’’ data for drawing inferences in production.(28) The idea behind doubly anonymized data is that not only are the identities of the individuals anonymized but the identity of the features are anonymized (e.”
Explaining Data-Driven Document Classifications (2014, background): “For example, it may not be obvious, but classifying web users based on the web pages they visit (Provost et al. 2009) could be cast in the same framework as document classification.”
Adnostic: Privacy Preserving Targeted Advertising (2010, background): “Most behavioral targeting systems work by categorizing users into one or more audience segments usually based on the data collected about their browsing behavior but possibly on search terms [45] or even placement in a social network [35] .”
Optimized Cost per Click in Taobao Display Advertising (2017, unknown): “First, mining user preference inferred from its behavior data and the ad item’s details, Taobao targeting system [17, 18] trains models to filter mass amount of ads for each page view (PV) request, which is called matching stage.”
Real-time bidding for online advertising: measurement and analysis (2013, unknown): “In ad networks advertisers largely adopt the cost-per-click (CPC) or cost-per-acquisition (CPA) pricing models where they only pay when certain goal is achieved.”
Machine learning for targeted display advertising: transfer learning in action (2013, background): “Other aspects of the system have been presented previously in conference publications [16,17, 19], and with few exceptions these will be treated quickly in this paper.”
Social Network Data Analytics (2011, methodology): “While each iteration in the original KL algorithm [54] had a complexity of O(|E| log |E|), Fiduccia and Mattheyses improved it to O(|E|) per iteration using appropriate data structures.”
GOTCHA! Network-Based Fraud Detection for Social Security Fraud (2017, methodology): “A similar approach is taken in Provost et al. (2009) to compute brand affinity, measuring the proximity of a node to the seed nodes.”
Social Network Analysis and Mining for Business Applications (2011, background): “For example, Provost et al. [2009] construct “quasisocial networks” from online visitations to social network pages and use a predictive modeling framework for advertising.”
Evaluating and Optimizing Online Advertising: Forget the Click, but There Are Good Proxies (2015, unknown): “Web site visitation is widely believed to reveal user preferences, and to improve targeting [14, 17].”