A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation

TL;DR

This paper investigates the use of generative models like GANs, VAEs, and GMMs to augment social network advertising data, improving predictive model performance and providing comparative insights on augmentation techniques.

Contribution

It introduces a generative augmentation framework for social network advertising data and compares the effectiveness of different models to guide practitioners.

Findings

Data augmentation improves classifier performance.

GANs, VAEs, and GMMs each offer different benefits.

Synthetic data alleviates small or imbalanced dataset issues.

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.