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Click Prediction in Digital Advertisements: A Fuzzy Approach to Model Selection

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Intelligent and Fuzzy Techniques: Smart and Innovative Solutions (INFUS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1197))

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Abstract

The fuzzy logic theorem is inherently used effectively in expressing current life problems. So, using fuzzy logic in machine learning is getting popular. In machine learning problems, especially using digital advertisement data, products/objects are being trained and predicted together, but this can cause worse prediction performance. A significant commitment of our research is, we propose a new approach for ensembling prediction with fuzzy clustering in this study. This approach aims to solve this problem. It also enables flexible clustering for the objects which have more than one cluster’s characteristics. On the other hand, our approach allows us ensembling boosting algorithms which are different types of ensembling and very popular in machine learning because of their successful performance in the literature. For testing our approach, we used an online travel agency’s digital advertisements data for predicting each hotel’s next day click amount, which is crucial for predicting marketing cost. The results show that ensembling the algorithms with a fuzzy approach has better performance result than applying algorithms individually.

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Author information

Authors and Affiliations

  1. Department of Management Engineering, Istanbul Technical University, Istanbul, Turkey

    Ahmet Tezcan Tekin, Tolga Kaya & Ferhan Çebi

Authors
  1. Ahmet Tezcan Tekin
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  2. Tolga Kaya
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  3. Ferhan Çebi
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Corresponding author

Correspondence to Ahmet Tezcan Tekin .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Sezi Cevik Onar

  3. Department of Industrial Engineering, Istanbul Technical University, İstanbul, Turkey

    Basar Oztaysi

  4. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Irem Ucal Sari

  5. Industrial Engineering Department, Yildiz Technical University, Istanbul, Turkey

    Selcuk Cebi

  6. Industrial Engineering Department, Galatasaray University, Istanbul, Turkey

    A. Cagri Tolga

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Tekin, A.T., Kaya, T., Çebi, F. (2021). Click Prediction in Digital Advertisements: A Fuzzy Approach to Model Selection. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., Tolga, A. (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing, vol 1197. Springer, Cham. https://doi.org/10.1007/978-3-030-51156-2_26

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