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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Bibliographische Detailangaben

Veröffentlicht in:	IEEE access 2021, Vol.9, p.83340-83354
Hauptverfasser:	Shi, Bichen, Tragos, Elias Z., Ozsoy, Makbule Gulcin, Dong, Ruihai, Hurley, Neil, Smyth, Barry, Lawlor, Aonghus
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Biological system modeling click through ratio Computational modeling Computer architecture distributed learning Machine learning Privacy Recommender systems Reinforcement learning Servers Training User profiles
Online-Zugang:	Volltext
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