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Found 8 papers, 1 papers with code
Robust Federated Contrastive Recommender System against Model Poisoning Attack
In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec.
Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation
Empirical results demonstrate that PTIA poses a significant threat to users' historical trajectories.
Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion
Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences.
Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution
The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient solution.
Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation
As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalized IoT service providers have to put unlearning functionality into their consideration.
Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent
Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating.
FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection
Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings.
Try This Instead: Personalized and Interpretable Substitute Recommendation
Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i. e., personalization) and item-item relationships (i. e., substitution) for recommendation.
