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

Ahmet Tezcan Tekin; Tolga Kaya; Ferhan Çebi

doi:10.1007/978-3-030-51156-2_26

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

Ahmet Tezcan Tekin^*, Tolga Kaya, Ferhan Çebi

^*Corresponding author for this work

Department of Management Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Citations (Scopus)

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.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques
Subtitle of host publication	Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A. Cagri Tolga
Publisher	Springer
Pages	213-220
Number of pages	8
ISBN (Print)	9783030511555
DOIs	https://doi.org/10.1007/978-3-030-51156-2_26
Publication status	Published - 2021
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2020 - Istanbul, Turkey Duration: 21 Jul 2020 → 23 Jul 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1197 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2020
Country/Territory	Turkey
City	Istanbul
Period	21/07/20 → 23/07/20

Bibliographical note

Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Click prediction
Ensemble learning
Fuzzy model selection
Fuzzy regression
OTA

Access to Document

10.1007/978-3-030-51156-2_26

Cite this

Tekin, A. T., Kaya, T., & Çebi, F. (2021). Click Prediction in Digital Advertisements: A Fuzzy Approach to Model Selection. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference (pp. 213-220). (Advances in Intelligent Systems and Computing; Vol. 1197 AISC). Springer. https://doi.org/10.1007/978-3-030-51156-2_26

Tekin, Ahmet Tezcan ; Kaya, Tolga ; Çebi, Ferhan. / Click Prediction in Digital Advertisements : A Fuzzy Approach to Model Selection. Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A. Cagri Tolga. Springer, 2021. pp. 213-220 (Advances in Intelligent Systems and Computing).

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title = "Click Prediction in Digital Advertisements: A Fuzzy Approach to Model Selection",

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{\textquoteright}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{\textquoteright}s digital advertisements data for predicting each hotel{\textquoteright}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.",

keywords = "Click prediction, Ensemble learning, Fuzzy model selection, Fuzzy regression, OTA",

author = "Tekin, {Ahmet Tezcan} and Tolga Kaya and Ferhan {\c C}ebi",

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publisher = "Springer",

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editor = "Cengiz Kahraman and {Cevik Onar}, Sezi and Basar Oztaysi and Sari, {Irem Ucal} and Selcuk Cebi and Tolga, {A. Cagri}",

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Tekin, AT, Kaya, T & Çebi, F 2021, Click Prediction in Digital Advertisements: A Fuzzy Approach to Model Selection. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference. Advances in Intelligent Systems and Computing, vol. 1197 AISC, Springer, pp. 213-220, International Conference on Intelligent and Fuzzy Systems, INFUS 2020, Istanbul, Turkey, 21/07/20. https://doi.org/10.1007/978-3-030-51156-2_26

Click Prediction in Digital Advertisements: A Fuzzy Approach to Model Selection. / Tekin, Ahmet Tezcan; Kaya, Tolga ; Çebi, Ferhan.
Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A. Cagri Tolga. Springer, 2021. p. 213-220 (Advances in Intelligent Systems and Computing; Vol. 1197 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Click Prediction in Digital Advertisements

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2020

AU - Tekin, Ahmet Tezcan

AU - Kaya, Tolga

AU - Çebi, Ferhan

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Click prediction

KW - Ensemble learning

KW - Fuzzy model selection

KW - Fuzzy regression

KW - OTA

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U2 - 10.1007/978-3-030-51156-2_26

DO - 10.1007/978-3-030-51156-2_26

M3 - Conference contribution

AN - SCOPUS:85088747691

SN - 9783030511555

T3 - Advances in Intelligent Systems and Computing

SP - 213

EP - 220

BT - Intelligent and Fuzzy Techniques

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Cebi, Selcuk

A2 - Tolga, A. Cagri

PB - Springer

Y2 - 21 July 2020 through 23 July 2020

ER -

Tekin AT, Kaya T , Çebi F. Click Prediction in Digital Advertisements: A Fuzzy Approach to Model Selection. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Cebi S, Tolga AC, editors, Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference. Springer. 2021. p. 213-220. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-51156-2_26

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

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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