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Probabilistic macro behavioral targeting

Published: 29 October 2012 Publication History

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.

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  1. Probabilistic macro behavioral targeting

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    cover image ACM Conferences
    DUBMMSM '12: Proceedings of the 2012 workshop on Data-driven user behavioral modelling and mining from social media
    October 2012
    46 pages
    ISBN:9781450317078
    DOI:10.1145/2390131
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 29 October 2012

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    Author Tags

    1. bayesian network
    2. behavioral targeting

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    • (2018)Topic modeling in marketing: recent advances and research opportunitiesJournal of Business Economics10.1007/s11573-018-0915-7Online publication date: 15-Sep-2018
    • (2017)SILVERBACK+Knowledge and Information Systems10.1007/s10115-016-0962-850:3(969-997)Online publication date: 1-Mar-2017
    • (2014)SILVERBACK: Scalable association mining for temporal data in columnar probabilistic databases2014 IEEE 30th International Conference on Data Engineering10.1109/ICDE.2014.6816724(1072-1083)Online publication date: Mar-2014

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