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Found 15 papers, 5 papers with code
Multi-Task Learning with Loop Specific Attention for CDR Structure Prediction
Compared with the other CDR loops, that is the H1 and H2 loops, the CDR structure of the H3 loop is more challenging due to its varying length and flexible structure.
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.
A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation
In this article, we propose a two-phase CR algorithm that incorporates the geographical influence of POIs and is regularized based on the variance of POIs popularity and users' activities over time.
Cross-Domain Collaborative Filtering via Translation-based Learning
In our model, we learn the embedding space with translation vectors and capture high-order feature interactions in users' multiple preferences across domains.
A Neural Attention Model for Adaptive Learning of Social Friends' Preferences
Furthermore, we study the effect of the proposed social behavioral attention mechanism and show that it is a key factor to our model's performance.
Adaptive Deep Learning of Cross-Domain Loss in Collaborative Filtering
In this paper, we propose an adaptive deep learning strategy for cross-domain recommendation, referred to as ADC.
Leveraging Trust and Distrust in Recommender Systems via Deep Learning
We show that our deep learning strategy plays an important role in capturing the nonlinear correlations between user preferences and the social information of trust and distrust relationships, and demonstrate the importance of our social negative sampling strategy on the proposed model.
