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Found 9 papers, 5 papers with code
Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming Events
By considering a new event as a task, we design an actor-critic learning scheme to compute the optimal policy on estimating the viewers' high-bandwidth connections.
Sequence Adaptation via Reinforcement Learning in Recommender Systems
This might limit the recommendation accuracy, as in practice users follow different trends on the sequential recommendations.
A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming
We first formulate the user experience prediction problem as a classification task, accounting for the fact that most of the viewers at the beginning of an event have poor quality of experience due to low-bandwidth connections and limited interactions with the tracker.
Multi-Task Learning for User Engagement and Adoption in Live Video Streaming Events
Nowadays, live video streaming events have become a mainstay in viewer's communication in large international enterprises.
EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming Events
We evaluate our proposed model on the link prediction task on three real-world datasets, generated by live video streaming events.
VStreamDRLS: Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming Solutions
We propose VStreamDRLS, a graph neural network architecture with a self-attention mechanism to capture the evolution of the graph structure of live video streaming events.
Distill2Vec: Dynamic Graph Representation Learning with Knowledge Distillation
In this study we propose Distill2Vec, a knowledge distillation strategy to train a compact model with a low number of trainable parameters, so as to reduce the latency of online inference and maintain the model accuracy high.
Adaptive Neural Architectures for Recommender Systems
The main drawback of deep reinforcement strategies is that are based on predefined and fixed neural architectures.
On Estimating the Training Cost of Conversational Recommendation Systems
Conversational recommendation systems have recently gain a lot of attention, as users can continuously interact with the system over multiple conversational turns.
