Probabilistic macro behavioral targeting

Yusheng Xie*, Yi Gao, Jiangtao Gou, Yu Cheng, Daniel Honbo, Kunpeng Zhang, Ankit Agrawal, Alok Nidhi Choudhary

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We investigate a class of emerging online marketing challenges in social networks; and formally, we define macro behavioral targeting (MBT) to be the marketing efforts that appeal to a massive targeted population with non-personalized broadcasting. Upon the problem formulation, we describe a probabilistic graphical model for MBT. In our model, we derive the prior distributions from scratch because existing applications of graphical model / Bayesian network cannot fully capture the unique characteristics of MBT. In the derivation, we propose an approximation method to circumvent an intractable situation where order statistics need be calculated from exponentially increasing computations. In the experiments, we present case studies on real Facebook data.

Original languageEnglish (US)
Title of host publicationDUBMMSM'12 - Proceedings of the 2012 ACM Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, Co-located with CIKM 2012
Pages7-10
Number of pages4
DOIs
StatePublished - Dec 10 2012
Event2012 ACM Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, DUBMMSM 2012, Co-located with 21st ACM Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Oct 29 2012

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other2012 ACM Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, DUBMMSM 2012, Co-located with 21st ACM Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period10/29/1210/29/12

Keywords

  • Bayesian network
  • Behavioral targeting

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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