Stochastic Blockmodeling for Online Advertising

نویسندگان

  • Li Chen
  • Matthew Patton
چکیده

Online advertising is market communication over the internet. This form of advertising has proven its importance in the golden digital age. In this work, we approach the business problem of improving the effectiveness of ad campaigns and targeting a wider range of online audience via discovering the intrinsic structures of the websites. We introduce a stochastic blockmodeling framework for discovering the website structures, propose two vertex clustering algorithms based on the Bayesian information criterion, and compare the performance with a goodness-of-fit method and a deterministic graph partitioning method. We demonstrate the effectiveness of our proposed algorithms on simulation and the AOL website dataset.

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تاریخ انتشار 2015