DS-ADMM++: A Novel Distributed Quantized ADMM to Speed up Differentially Private Matrix Factorization

Matrix factorization is a powerful method to implement collaborative filtering recommender systems. This article addresses two major challenges, privacy and efficiency, which matrix factorization is facing. We based our work on DS-ADMM, a distributed matrix factorization algorithm with decent effici...

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Bibliographic Details

Published in:	IEEE transactions on parallel and distributed systems Vol. 33; no. 6; pp. 1289 - 1302
Main Authors:	Zhang, Feng, Xue, Erkang, Guo, Ruixin, Qu, Guangzhi, Zhao, Gansen, Zomaya, Albert Y.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.06.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	ADMM Algorithms Charge coupled devices Costs Differential privacy Efficiency Factorization Machine learning Matrix decomposition Matrix factorization parallel and distributed computing Privacy quantization Quantization (signal) Recommender systems
ISSN:	1045-9219, 1558-2183
Online Access:	Get full text
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