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Open AccessArticle

Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks

1
Department of Computer Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea
2
Department of Communication, Department of Computer Science, William Paterson University of New Jersey, 300 Pompton Rd, Wayne, NJ 07470, USA
*
Authors to whom correspondence should be addressed.
Sensors 2021, 21(1), 199; https://doi.org/10.3390/s21010199
Received: 1 December 2020 / Revised: 19 December 2020 / Accepted: 22 December 2020 / Published: 30 December 2020
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter of online advertisements while enhancing relevance to consumers to generate favorable responses. Although most user interest classification studies have focused on textual data, the combined analysis of images and texts on user-generated posts can more precisely predict a consumer’s interests. Therefore, this research classifies SNS users’ interests by utilizing both texts and images. Consumers’ interests were defined using the Curlie directory, and various convolutional neural network (CNN)-based models and recurrent neural network (RNN)-based models were tested for our user interest classification system. In our hybrid neural network (NN) model, CNN-based classification models were used to classify images from users’ SNS postings while RNN-based classification models were used to classify textual data. The results of our extensive experiments show that the classification of users’ interests performed best when using texts and images together, at 96.55%, versus texts only, 41.38%, or images only, 93.1%. Our proposed system provides insights into personalized SNS advertising research and informs marketers on making (1) interest-based recommendations, (2) ranked-order recommendations, and (3) real-time recommendations. View Full-Text
Keywords: SNS; personalized ads; deep learning; neural networks; interest classification SNS; personalized ads; deep learning; neural networks; interest classification
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MDPI and ACS Style

Hong, T.; Choi, J.-A; Lim, K.; Kim, P. Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks. Sensors 2021, 21, 199. https://doi.org/10.3390/s21010199

AMA Style

Hong T, Choi J-A, Lim K, Kim P. Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks. Sensors. 2021; 21(1):199. https://doi.org/10.3390/s21010199

Chicago/Turabian Style

Hong, Taekeun; Choi, Jin-A; Lim, Kiho; Kim, Pankoo. 2021. "Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks" Sensors 21, no. 1: 199. https://doi.org/10.3390/s21010199

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