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Found 5 papers, 2 papers with code
Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System
Leveraging these insights, we introduce the Vulnerability-aware Adversarial Training (VAT), designed to defend against poisoning attacks in recommender systems.
Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems
Recent studies have demonstrated the vulnerability of recommender systems to data poisoning attacks, where adversaries inject carefully crafted fake user interactions into the training data of recommenders to promote target items.
LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning Attacks
Traditional defense strategies predominantly depend on predefined assumptions or rules extracted from specific known attacks, limiting their generalizability to unknown attack types.
Robust Recommender System: A Survey and Future Directions
With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload.
DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph
Unsupervised representation learning for dynamic graphs has attracted a lot of research attention in recent years.
