DARES: An Asynchronous Distributed Recommender System Using Deep Reinforcement Learning

DARES: An Asynchronous Distributed Recommender System Using Deep Reinforcement Learning

Traditional Recommender Systems (RS) use central servers to collect user data, compute user profiles and train global recommendation models. Central computation of RS models has great results in performance because the models are trained using all the available information and the full user profiles...

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Bibliographic Details
Main Authors: Bichen Shi, Elias Z. Tragos, Makbule Gulcin Ozsoy, Ruihai Dong, Neil Hurley, Barry Smyth, Aonghus Lawlor
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Recommender systems
reinforcement learning
distributed learning
click through ratio
Online Access:https://ieeexplore.ieee.org/document/9448142/

Internet

https://ieeexplore.ieee.org/document/9448142/

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