Efficient Integration of Reinforcement Learning in Graph Neural Networks-Based Recommender Systems

Efficient Integration of Reinforcement Learning in Graph Neural Networks-Based Recommender Systems

Recommendation systems have advanced significantly in recent years, achieving greater accuracy and relevance. However, traditional approaches often suffer from a mismatch between the losses used during training and the metrics used for evaluation. Models are typically trained to minimize a loss func...

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Bibliographic Details
Main Authors: Abdurakhmon Sharifbaev, Mikhail Mozikov, Hakimjon Zaynidinov, Ilya Makarov
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Recommendation system
graph neural networks
reinforcement learning
double deep Q-networks
Online Access:https://ieeexplore.ieee.org/document/10795136/
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