A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation
arXiv:2404.13812 · doi:10.1109/MLISE62164.2024.10674203
Abstract
In the ever-evolving landscape of social network advertising, the volume and accuracy of data play a critical role in the performance of predictive models. However, the development of robust predictive algorithms is often hampered by the limited size and potential bias present in real-world datasets. This study presents and explores a generative augmentation framework of social network advertising data. Our framework explores three generative models for data augmentation - Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Gaussian Mixture Models (GMMs) - to enrich data availability and diversity in the context of social network advertising analytics effectiveness. By performing synthetic extensions of the feature space, we find that through data augmentation, the performance of various classifiers has been quantitatively improved. Furthermore, we compare the relative performance gains brought by each data augmentation technique, providing insights for practitioners to select appropriate techniques to enhance model performance. This paper contributes to the literature by showing that synthetic data augmentation alleviates the limitations imposed by small or imbalanced datasets in the field of social network advertising. At the same time, this article also provides a comparative perspective on the practicality of different data augmentation methods, thereby guiding practitioners to choose appropriate techniques to enhance model performance.
Accepted by 2024 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)
References in corpus (10)
- Auto-Encoding Variational Bayes
- DeepInf: Social Influence Prediction with Deep Learning
- Exploring Diverse Methods in Visual Question Answering
- Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers
- Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN
- Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks
- Regional Style and Color Transfer
- Deep Learning for Content-based Personalized Viewport Prediction of 360-Degree VR Videos
- AD-Aligning: Emulating Human-like Generalization for Cognitive Domain Adaptation in Deep Learning
- Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systems