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http://dx.doi.org/10.5392/JKCA.2020.20.03.267

Design of a Multi-array CNN Model for Improving CTR Prediction  

Kim, Tae-Suk (배재대학교 경영학과)
Publication Information
The Journal of the Korea Contents Association / v.20, no.3, 2020 , pp. 267-274 More about this Journal
Abstract
Click-through rate (CTR) prediction is an estimate of the probability that a user will click on a given item and plays an important role in determining strategies for maximizing online ad revenue. Recently, research has been performed to utilize CNN for CTR prediction. Since the CTR data does not have a meaningful order in terms of correlation, the CTR data may be arranged in any order. However, because CNN only learns local information limited by filter size, data arrays can have a significant impact on performance. In this paper, we propose a multi-array CNN model that generates a data array set that can extract all local feature information that CNN can collect, and learns features through individual CNN modules. Experimental results for large data sets show that the proposed model achieves a 22.6% synergy with RI in AUC compared to the existing CNN, and the proposed array generation method achieves 3.87% performance improvement over the random generation method.
Keywords
CTR; CNN; Feature Generation; Deep Learning;
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