A Distributed Asynchronous Deep Reinforcement Learning Framework for Recommender Systems

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Title: A Distributed Asynchronous Deep Reinforcement Learning Framework for Recommender Systems
Authors: Shi, BichenTragos, EliasOzsoy, Makbule GulcinDong, RuihaiSmyth, BarryHurley, Neil J.Lawlor, Aonghus
Permanent link: http://hdl.handle.net/10197/12220
Date: 26-Sep-2020
Online since: 2021-05-27T11:57:43Z
Abstract: In this paper we propose DADRL, a distributed, asynchronous reinforcement learning recommender system based on the asynchronous advantage actor-critic model (A3C), which combines ideas from A3C and federated learning (FL). The proposed algorithm keeps the user preferences or interactions on local devices and uses a combination of on-device, local recommendation models and a complementary global model. The global model is trained only by the loss gradients of the local models, rather than directly using user preferences or interactions data. We demonstrate, using well-known datasets and benchmark algorithms, how this approach can deliver performance that is comparable with the current state-of-the-art while enhancing user privacy.
Funding Details: Science Foundation Ireland
Funding Details: Insight Research Centre
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2018 Association for Computing Machinery
Keywords: Novel personal sensingReinforcement learningRecommender systemsDistributed learning
DOI: 10.1145/3383313
Other versions: https://recsys.acm.org/recsys20/
Language: en
Status of Item: Peer reviewed
Conference Details: The 14th ACM Conference on Recommender Systems (RecSys 2020), Online, 22-26 September 2020
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Insight Research Collection

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