2 citations · 4 across the 5 of their papers we have counts for
3 papers · 1 filter
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems
Francesco Fabbri, Xianghang Liu, Jack R. McKenzie +2
Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy. Instead of sending privat…
MM-GEF: Multi-modal representation meet collaborative filtering
Hao Wu, Alejandro Ariza-Casabona, Bartłomiej Twardowski +1
In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses…
Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation
Alejandro Ariza-Casabona, Bartlomiej Twardowski, Tri Kurniawan Wijaya
Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input…