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Advertisement Allocation and Mechanism Design in Native Stream Advertising

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Complex Networks and Their Applications VII (COMPLEX NETWORKS 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 813))

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Abstract

We study optimal advertisement allocation in native stream advertising, which exhibits notable fundamental differences from traditional web advertising. Our model highlights the influence of distance between consecutively projected ads on user engagement, which implies interesting negative externalities between ads: when there are fewer intervening posts between two ads, there is higher ad fatigue and lower user engagement. We study the problem of advertisement selection and placement in a stream so as to maximize social welfare. We fully characterize the computational complexity of the problem by demonstrating that it is tightly connected to a special form of interval scheduling. Next, we prove that it is strictly NP-hard (SNP-hard) but can be efficiently approximated to within 1 / 2 of the optimum. We complement this result by studying the mechanism design variant of the problem and develop a 1 / 2-approximation truthful-in-expectation mechanism. We also consider mechanisms that guarantee a stronger deterministic form of truthfulness. We believe that these results lay a valuable theoretical foundation for further research in the field.

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Acknowledgments

I. Koutsopoulos acknowledges the support of the project “Research Reinforcement” by the General Secretariat for Research and Technology (GSRT).

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Authors and Affiliations

  1. Amazon Research, Haifa, Israel

    Iftah Gamzu

  2. Department of Informatics, Athens University of Economics and Business, Athens, Greece

    Iordanis Koutsopoulos

Authors
  1. Iftah Gamzu
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  2. Iordanis Koutsopoulos
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Corresponding author

Correspondence to Iordanis Koutsopoulos .

Editor information

Editors and Affiliations

  1. Nokia Bell Labs, Cambridge, UK

    Luca Maria Aiello

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I UMR CNRS 6306 9, University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  4. Mathematical Institute, University of Oxford, Oxford, UK

    Renaud Lambiotte

  5. Department of Computer Science and Technology, The Computer Laboratory, University of Cambridge, Cambridge, UK

    Pietro Lió

  6. Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

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Gamzu, I., Koutsopoulos, I. (2019). Advertisement Allocation and Mechanism Design in Native Stream Advertising. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_16

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