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Evaluation of Tensor-Based Algorithms for Real-Time Bidding Optimization

  • Andrzej Szwabe
  • Paweł Misiorek
  • Michał Ciesielczyk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10191)

Abstract

In this paper we evaluate tensor-based approaches to the Real-Time Bidding (RTB) Click-Through Rate (CTR) estimation problem. We propose two new tensor-based CTR prediction algorithms. We analyze the evaluation results collected from several papers – obtained with the use of the iPinYou contest dataset and the Area Underneath the ROC curve measure. We accompany these results with analogical results of our experiments – conducted with the use of our implementations of tensor-based algorithms and approaches based on the logistic regression. In contrast to the results of other authors, we show that biases – in particular those being low-order expectation value estimates – are at least as useful as outcomes of high-order components’ processing. Moreover, on the basis of Average Precision results, we postulate that ROC curve should not be the only characteristic used to evaluate RTB CTR estimation performance.

Keywords

Big Data Context-aware recommendation Tensor decomposition Logistic regression Click-Through Rate prediction WWW Display advertising Real-Time Bidding Demand-Side Platform 

Notes

Acknowledgments

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

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andrzej Szwabe
    • 1
  • Paweł Misiorek
    • 1
  • Michał Ciesielczyk
    • 1
  1. 1.Institute of Control and Information EngineeringPoznan University of TechnologyPoznanPoland

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