Search Results for author:
Found 2 papers, 1 papers with code
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems
Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy.
Fast Approximate Bayesian Contextual Cold Start Learning (FAB-COST)
Cold-start is a notoriously difficult problem which can occur in recommendation systems, and arises when there is insufficient information to draw inferences for users or items.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
