Generating Ad Targeting Rules using Sparse Principal Component Analysis with Constraints

Publication
Apr 7, 2014
Abstract

Determining the right audience for an advertising campaign is a well-established problem of central importance to many Internet companies. Two distinct targeting approaches exist, the model-based approach, which leverages machine learning, and the rule-based approach, which relies on manual generation of targeting rules. Common rules include identifying users that had interactions (website visits, emails received, etc.) with the companies related to the advertiser, or search queries related to their product. We consider a problem of discovering such rules from data using Constrained Sparse PCA. The constraints are put in place to account for cases when evidence in data suggests a relation that is not appropriate for advertising. Experiments on real-world data indicate the potential of the proposed approach.

  • 23rd International World Wide Web Conference, WWW 14
  • Conference/Workshop Paper

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