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Found 11 papers, 7 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.
IDEA: Invariant Defense for Graph Adversarial Robustness
To address these limitations, we analyze the causalities in graph adversarial attacks and conclude that causal features are key to achieve graph adversarial robustness, owing to their determinedness for labels and invariance across attacks.
Popularity Debiasing from Exposure to Interaction in Collaborative Filtering
In this paper, we propose a new criterion for popularity debiasing, i. e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion.
Graph Adversarial Immunization for Certifiable Robustness
In this paper, we propose and formulate graph adversarial immunization, i. e., vaccinating part of graph structure to improve certifiable robustness of graph against any admissible adversarial attack.
Adversarial Camouflage for Node Injection Attack on Graphs
In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes.
Single Node Injection Attack against Graph Neural Networks
In this paper, we focus on an extremely limited scenario of single node injection evasion attack, i. e., the attacker is only allowed to inject one single node during the test phase to hurt GNN's performance.
Investigating Attention Mechanism in 3D Point Cloud Object Detection
Object detection in three-dimensional (3D) space attracts much interest from academia and industry since it is an essential task in AI-driven applications such as robotics, autonomous driving, and augmented reality.
INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering
INMO generates the inductive embeddings for users (items) by characterizing their interactions with some template items (template users), instead of employing an embedding lookup table.
