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

  • Andrzej Szwabe
  • Pawel Misiorek
  • Michal Ciesielczyk
Conference paper

DOI: 10.1007/978-3-319-58274-0_2

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

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.

Keywords

Big data Multidimensional data modeling Context-aware recommendation Data extraction Data mining Logistic regression Click-through rate estimation WWW Real-Time Bidding 

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andrzej Szwabe
    • 1
  • Pawel Misiorek
    • 1
  • Michal Ciesielczyk
    • 1
  1. 1.Institute of Control and Information EngineeringPoznan University of TechnologyPoznanPoland

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