Fusion Enhanced Click-Through-Rate Prediction

Date

2023-10-24

Author

Bayraktar, Murat
Gökce, Fatma Ceyda
Aksu, Dogukan
Altıngövde, İsmail Sengör
Karagöz, Pınar
Toroslu, İsmail Hakkı

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this study, the effects of combining multiple models to increase the accuracy of Click-Through Rate (CTR) prediction, which is a critical task in online advertising, product marketing, and recommendation systems, have been examined. Traditional CTR prediction methods use a single model developed for this purpose and therefore cannot capture some complex relationships. In this study, the aim is to increase the accuracy of CTR prediction in terms of different metrics by combining multiple models using the ranx library. The experimental results show that the proposed method achieve better results than CTR prediction models based on a single model used in previous studies. These results indicate that the development of different and new combination methods could also be beneficial.

Subject Keywords

Click-Through Rate (CTR), Fusion, Online advertising, Ranking

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85173539591&origin=inward
https://hdl.handle.net/11511/110116

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Department of Electrical and Electronics Engineering, Conference / Seminar