Popularity Score

Popularity scoring aims to quantify the appeal of various entities, from anime and songs to stock prices and social media posts, enabling prediction and informed decision-making. Current research focuses on developing sophisticated models, including deep neural networks (like those incorporating GPT-2 and ResNet-50), graph attention networks, and various machine learning algorithms (e.g., Random Forest, XGBoost), to accurately predict popularity based on diverse multimodal data (text, images, audio, user behavior). These advancements have implications across numerous fields, improving recommendation systems, resource allocation in creative industries, and even anomaly detection in network security by identifying patterns associated with popularity and its potential biases.