Probabilistic macro behavioral targeting

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

doi:10.1145/2390131.2390135

Probabilistic macro behavioral targeting

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

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 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 language	English (US)
Title of host publication	DUBMMSM'12 - Proceedings of the 2012 ACM Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, Co-located with CIKM 2012
Pages	7-10
Number of pages	4
DOIs	https://doi.org/10.1145/2390131.2390135
State	Published - 2012
Event	2012 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 2012 → Oct 29 2012

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings

Other

Other	2012 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
Country/Territory	United States
City	Maui, HI
Period	10/29/12 → 10/29/12

Keywords

Bayesian network
Behavioral targeting

ASJC Scopus subject areas

General Business, Management and Accounting
General Decision Sciences

Access to Document

10.1145/2390131.2390135

Cite this

Xie, Y., Gao, Y., Gou, J., Cheng, Y., Honbo, D., Zhang, K., Agrawal, A., & Choudhary, A. (2012). Probabilistic macro behavioral targeting. In DUBMMSM'12 - Proceedings of the 2012 ACM Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, Co-located with CIKM 2012 (pp. 7-10). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/2390131.2390135

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title = "Probabilistic macro behavioral targeting",

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.",

keywords = "Bayesian network, Behavioral targeting",

author = "Yusheng Xie and Yi Gao and Jiangtao Gou and Yu Cheng and Daniel Honbo and Kunpeng Zhang and Ankit Agrawal and Alok Choudhary",

year = "2012",

doi = "10.1145/2390131.2390135",

language = "English (US)",

isbn = "9781450317078",

series = "International Conference on Information and Knowledge Management, Proceedings",

pages = "7--10",

booktitle = "DUBMMSM'12 - Proceedings of the 2012 ACM Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, Co-located with CIKM 2012",

note = "2012 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 ; Conference date: 29-10-2012 Through 29-10-2012",

}

Xie, Y, Gao, Y, Gou, J, Cheng, Y, Honbo, D, Zhang, K, Agrawal, A & Choudhary, A 2012, Probabilistic macro behavioral targeting. in DUBMMSM'12 - Proceedings of the 2012 ACM Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, Co-located with CIKM 2012. International Conference on Information and Knowledge Management, Proceedings, pp. 7-10, 2012 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, 10/29/12. https://doi.org/10.1145/2390131.2390135

Probabilistic macro behavioral targeting. / Xie, Yusheng; Gao, Yi; Gou, Jiangtao et al.
DUBMMSM'12 - Proceedings of the 2012 ACM Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, Co-located with CIKM 2012. 2012. p. 7-10 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Probabilistic macro behavioral targeting

AU - Xie, Yusheng

AU - Gao, Yi

AU - Gou, Jiangtao

AU - Cheng, Yu

AU - Honbo, Daniel

AU - Zhang, Kunpeng

AU - Agrawal, Ankit

AU - Choudhary, Alok

PY - 2012

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N2 - 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.

AB - 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.

KW - Bayesian network

KW - Behavioral targeting

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ER -

Xie Y, Gao Y, Gou J, Cheng Y, Honbo D, Zhang K et al. Probabilistic macro behavioral targeting. In DUBMMSM'12 - Proceedings of the 2012 ACM Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, Co-located with CIKM 2012. 2012. p. 7-10. (International Conference on Information and Knowledge Management, Proceedings). doi: 10.1145/2390131.2390135

Probabilistic macro behavioral targeting

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Keywords

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