Shilling Recommender Systems by Generating Side-feature-aware Fake User Profiles
This addresses a security vulnerability in recommender systems for users and platforms, but it is incremental as it builds on an existing framework.
The paper tackles the problem of shilling attacks on recommender systems that use side features, extending the Leg-UP framework to generate side-feature-aware fake user profiles, achieving strong attack performance and stealthiness in benchmarks.
Recommender systems (RS) greatly influence users' consumption decisions, making them attractive targets for malicious shilling attacks that inject fake user profiles to manipulate recommendations. Existing shilling methods can generate effective and stealthy fake profiles when training data only contain rating matrix, but they lack comprehensive solutions for scenarios where side features are present and utilized by the recommender. To address this gap, we extend the Leg-UP framework by enhancing the generator architecture to incorporate side features, enabling the generation of side-feature-aware fake user profiles. Experiments on benchmarks show that our method achieves strong attack performance while maintaining stealthiness.