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

Abstract

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.

HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks

“…As shown in Section 2, the first-order optimization methods determine its next search direction by referring the gradient of the loss function on the current weights. For this, the first-order methods compute its gradient using the first-order differential as explained in Equation (5). Many optimization methods to train not only the neural networks but also diverse machine learning models have been designed based on the first-order methods.…”

“…In particular, modern neural network models are consisted of deeper layers and more weights than traditional ones to maximize their performance. Accordingly, the latest deep learning models have shown notable abilities in many real-world applications, for example, computer visions (CV) [1,2], data analysis [3,4], personalized services [5,6], internet of things (IoT) [7,8], and natural language processing (NLP) [9,10], et al Among them, particularly, the CV task involving image classification and image semantic segmentation is one of the applications in which the deep learning models have been most actively used. Accordingly, many studies to improve the image processing ability of CNNs are being actively conducted.…”

Analysis of e-Mail Spam Detection Using a Novel Machine Learning-Based Hybrid Bagging Technique

“…Our suggested approach helps marketers make (1) interest-based suggestions, (2) ranked-order suggestions, and (3) real-time suggestions by giving insight into tailored SNS marketing communications. To their understanding, this is one of the earliest articles to leverage combined image and message statistics using user-generated material to enhance the effectiveness of reliably identifying the political inclinations of SNS users for such aim of improving targeted advertising experiences [ 17 ].…”

Infodemiological Study on the Use of Face Masks during Covid-19: Comparing U.S. and Korea

“…Data mining of tweets provides an efficient way to survey a large number of participants in real-time. Therefore, Twitter has provided new opportunities for researchers examining public opinion in a wide array of timely topics, such as political campaigns and elections (Karami et al, 2018), climate change sentiment (Cody et al, 2015), vaccinations (Tavoschi et al, 2020), targeted advertising (Hong et al, 2021), and public health crises such as the Ebola outbreak (Lazard et al, 2015).…”