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International Conference: Beyond Databases, Architectures and Structures

BDAS 2017: Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation pp 16–27Cite as

Tensor-Based Modeling of Temporal Features for Big Data CTR Estimation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 716))

Abstract

In this paper we propose a simple tensor-based approach to temporal features modeling that is applicable as means for logistic regression (LR) enhancement. We evaluate experimentally the performance of an LR system based on the proposed model in the Click-Through Rate (CTR) estimation scenario involving processing of very large multi-attribute data streams. We compare our approach to the existing approaches to temporal features modeling from the perspective of the Real-Time Bidding (RTB) CTR estimation scenario. On the basis of an extensive experimental evaluation, we demonstrate that the proposed approach enables achieving an improvement of the quality of CTR estimation. We show this improvement in a Big Data application scenario of the Web user feedback prediction realized within an RTB Demand-Side Platform.

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Acknowledgments

This work is supported by the Polish National Science Centre, grant DEC-2011/01/D/ST6/06788.

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Authors and Affiliations

  1. Institute of Control and Information Engineering, Poznan University of Technology, ul. Piotrowo 3a, 60-965, Poznan, Poland

    Andrzej Szwabe, Pawel Misiorek & Michal Ciesielczyk

Authors
  1. Andrzej Szwabe
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  2. Pawel Misiorek
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  3. Michal Ciesielczyk
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Corresponding author

Correspondence to Pawel Misiorek .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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Szwabe, A., Misiorek, P., Ciesielczyk, M. (2017). Tensor-Based Modeling of Temporal Features for Big Data CTR Estimation. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation. BDAS 2017. Communications in Computer and Information Science, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-319-58274-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-58274-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58273-3

  • Online ISBN: 978-3-319-58274-0

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