8 papers
Sequence Adaptation via Reinforcement Learning in Recommender Systems
Stefanos Antaris, Dimitrios Rafailidis
Accounting for the fact that users have different sequential patterns, the main drawback of state-of-the-art recommendation strategies is that a fixed sequence length of user-item…
A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming
Stefanos Antaris, Dimitrios Rafailidis, Sarunas Girdzijauskas
In this paper we present a deep graph reinforcement learning model to predict and improve the user experience during a live video streaming event, orchestrated by an agent/tracker.…
Multi-Task Learning for User Engagement and Adoption in Live Video Streaming Events
Stefanos Antaris, Dimitrios Rafailidis, Romina Arriaza
Nowadays, live video streaming events have become a mainstay in viewer's communication in large international enterprises. Provided that viewers are distributed worldwide, the main…
Adaptive Neural Architectures for Recommender Systems
Dimitrios Rafailidis, Stefanos Antaris
Deep learning has proved an effective means to capture the non-linear associations of user preferences. However, the main drawback of existing deep learning architectures is that t…
EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming Events
Stefanos Antaris, Dimitrios Rafailidis, Sarunas Girdzijauskas
In this study, we present a dynamic graph representation learning model on weighted graphs to accurately predict the network capacity of connections between viewers in a live video…
VStreamDRLS: Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming Solutions
Stefanos Antaris, Dimitrios Rafailidis
Live video streaming has become a mainstay as a standard communication solution for several enterprises worldwide. To efficiently stream high-quality live video content to a large…