21 citations · 76 across the 10 of their papers we have counts for
5 papers · 1 filter
P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups
Ioannis Arapakis, Panagiotis Papadopoulos, Kleomenis Katevas +1
Distributed (or Federated) learning enables users to train machine learning models on their very own devices, while they share only the gradients of their models usually in a diffe…
Supervised Advantage Actor-Critic for Recommender Systems
Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis +1
Casting session-based or sequential recommendation as reinforcement learning (RL) through reward signals is a promising research direction towards recommender systems (RS) that max…
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
Dusan Stamenkovic, Alexandros Karatzoglou, Ioannis Arapakis +2
Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms…
Self-Supervised Reinforcement Learning for Recommender Systems
Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis +1
In session-based or sequential recommendation, it is important to consider a number of factors like long-term user engagement, multiple types of user-item interactions such as clic…
Graph Highway Networks
Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis +1
Graph Convolution Networks (GCN) are widely used in learning graph representations due to their effectiveness and efficiency. However, they suffer from the notorious over-smoothing…