Fellowship Final Report: Shillings’ Attacks Detection in Recommendation Systems Using Hybrid Adversarial Deep Learning
Recommendation systems are ubiquitous in our daily lives, from suggesting products on e-commerce platforms to recommending movies on streaming services. These systems work by predicting a user’s preferences based on their past behavior and feedback, such as ratings or reviews. However, these systems are vulnerable to malicious attacks, such as shilling attacks, where fake ratings […]
