58 citations · 163 across the 41 of their papers we have counts for
5 papers · 1 filter
Query Drift Compensation: Enabling Compatibility in Continual Learning of Retrieval Embedding Models
Dipam Goswami, Liying Wang, Bartłomiej Twardowski +1
Text embedding models enable semantic search, powering several NLP applications like Retrieval Augmented Generation by efficient information retrieval (IR). However, text embedding…
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…
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction
Xianghang Liu, Bartłomiej Twardowski, Tri Kurniawan Wijaya
In Federated Learning (FL) of click-through rate (CTR) prediction, users' data is not shared for privacy protection. The learning is performed by training locally on client devices…