Shilling Recommender Systems by Generating Side-feature-aware Fake User Profiles
NeutralArtificial Intelligence
A recent study discusses the vulnerabilities of recommender systems to shilling attacks, where fake user profiles are created to manipulate recommendations. While current methods can generate stealthy fake profiles using only rating data, they fall short when side features are involved. This research highlights the need for more comprehensive solutions to protect users from deceptive practices in digital recommendations.
— Curated by the World Pulse Now AI Editorial System

