Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

Xin Luo; Wen Qin; Ani Dong; Khaled Sedraoui; MengChu Zhou

doi:10.1109/JAS.2020.1003396

Volume 8 Issue 2

Feb. 2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(2): 402-411

Xin Luo, Wen Qin, Ani Dong, Khaled Sedraoui and MengChu Zhou, "Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 402-411, Feb. 2021. doi: 10.1109/JAS.2020.1003396

Citation:

Xin Luo, Wen Qin, Ani Dong, Khaled Sedraoui and MengChu Zhou, "Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 402-411, Feb. 2021. doi: 10.1109/JAS.2020.1003396

Citation:

Xin Luo, Wen Qin, Ani Dong, Khaled Sedraoui and MengChu Zhou, "Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 402-411, Feb. 2021. doi: 10.1109/JAS.2020.1003396

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Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

doi: 10.1109/JAS.2020.1003396

Funds: This work was supported in part by the National Natural Science Foundation of China (61772493), the Deanship of Scientific Research (DSR) at King Abdulaziz University (RG-48-135-40), Guangdong Province Universities and College Pearl River Scholar Funded Scheme (2019), and the Natural Science Foundation of Chongqing (cstc2019jcyjjqX0013)

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Author Bio:
Xin Luo (M’14–SM’17) received the B.S. degree in computer science from the University of Electronic Science and Technology of China, in 2005 and the Ph.D. degree in computer science from Beihang University, in 2011. In 2016, he joined the Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, as a Professor of computer science and engineering. He is currently also a Distinguished Professor of computer science with the Dongguan University of Technology. His current research interests include big data analysis and intelligent control. He has published over 100 papers (including over 60 IEEE Transactions papers) in the above areas. Dr. Luo was a recipient of the Hong Kong Scholar Program jointly by the Society of Hong Kong Scholars and China Post-Doctoral Science Foundation in 2014, the Pioneer Hundred Talents Program of Chinese Academy of Sciences in 2016, and the Advanced Support of the Pioneer Hundred Talents Program of Chinese Academy of Sciences in 2018. He is currently serving as an Associate Editor for IEEE/CAA Journal of Automatica Sinica and Neurocomputing. He has also served as the Program Committee Member for over 20 international conference

Wen Qin received the BS degree network engineering from China West Normal University, Nanchong, China, in 2017, and the ME degree in computer application technology from the China West Normal University, Nanchong, China, in 2020. She is currently studying for the Ph.D. degree in computer science from Chongqing University of Posts and Telecommunications (CQUPT) and jointly by the Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences. Her research interests include big data analysis and algorithm design for large-scale data applications

Ani Dong received the B.Sc. degree in industrial automation from Xi’an University of Architecture And Technology, in 2001 and the M.Sc. degree in control theory and control engineering from the same school in 2004. She has worked at the City College of Dongguan University of Technology since 2004 and is currently a Lecturer in the Computer and Information Department of the same college. She was a Visiting Scholar at Swinburne University of Technology in Australia from February 2018 to February 2019. Her research interests include intelligent algorithms

Khaled Sedraoui received the B.S. degree from the Institute of Technology and Sciences Tunis (ESSTT), Tunis, Tunisia, in 1989, the M.Sc. degree from the École de Technologie Supérieure, Quebec University, Montreal, Canada, in 1994, and the Ph.D. degree from ESSTT in 2010, all in electrical engineering. Since 1997, he has been a Faculty Member with the College of Technology, Jeddah, Saudi Arabia. He is currently an Assistant Professor of Electrical and Computer Engineering with the College of Engineering, King Abdulaziz University, Jeddah. His research interests include renewable energies integration and power quality improvement, design and control strategy for flexible ac transmission system, electric energy storage for R.E. systems and E.V., and optimization for smart grid

MengChu Zhou (S’88–M’90–SM’93–F’03) received the B.S. degree in control engineering from Nanjing University of Science and Technology, Nanjing, China in 1983, M.S. degree in automatic control from Beijing Institute of Technology, Beijing, China in 1986, and Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, Troy, NY, USA in 1990. He joined New Jersey Institute of Technology (NJIT), Newark, NJ, USA in 1990, and is now a Distinguished Professor of Electrical and Computer Engineering. He has over 800 publications including 12 books, 600+ journal papers (500+ in IEEE transactions), 27 patents and 29 book-chapters. His research interests include Petri nets, intelligent automation, Internet of Things, big data, web services, and intelligent transportation. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering and Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica. He served as Associate Editor of IEEE Transactions on Robotics and Automation, IEEE Transactions on Automation Science and Engineering, and IEEE Transactions on Industrial Informatics, and Editor of IEEE Transactions on Automation Science and Engineering. He served as a Guest-Editor for many journals including IEEE Internet of Things Journal, IEEE Transactions on Industrial Electronics, and IEEE Transactions on Semiconductor Manufacturing. He is presently Associate Editor of IEEE Transactions on Intelligent Transportation Systems, IEEE Internet of Things Journal, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Frontiers of Information Technology & Electronic Engineering. He was General Chair of IEEE Conference on Automation Science and Engineering, Washington D.C., August 23–26, 2008, General Co-Chair of 2003 IEEE International Conference on System, Man and Cybernetics (SMC), Washington D.C., October 5–8, 2003 and 2019 IEEE International Conference on SMC, Bari, Italy, Oct. 6–9, 2019, Founding General Co-Chair of 2004 IEEE International Conference on Networking, Sensing and Control, Taipei, China, March 21–23, 2004, and General Chair of 2006 IEEE International Conference on Networking, Sensing and Control, Ft. Lauderdale, Florida, USA. April 23–25, 2006. He was Program Chair of 2010 IEEE International Conference on Mechatronics and Automation, August 4–7, 2010, Xi’an, China, 1998 and 2001 IEEE International Conference on SMC and 1997 IEEE International Conference on Emerging Technologies and Factory Automation. Dr. Zhou has led or participated in over 50 research and education projects with total budget over $12M, funded by National Science Foundation, Department of Defense, NIST, New Jersey Science and Technology Commission, and industry. He was a recipient of Excellence in Research Prize and Medal from NJIT, Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, and Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE SMC Society, and Computer-Integrated Manufacturing UNIVERSITY-LEAD Award from Society of Manufacturing Engineers. He has been among the most highly cited scholars since 2012 and ranked top one in the field of engineering worldwide in 2012 by Web of Science. He is a recipient of Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man and Cybernetics Society. He is a Life Member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is a Fellow of International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS), Chinese Association of Automation (CAA), and National Academy of Inventors
Corresponding author: M. C. Zhou is with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA, and also with the Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21481, Saudi Arabia (e-mail: zhou@njit.edu)
Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JAS.2020.1003396
Received Date: 2020-05-04
Revised Date: 2020-06-04
Accepted Date: 2020-06-28

Available Online: 2020-07-16

Abstract

Abstract

A recommender system (RS) relying on latent factor analysis usually adopts stochastic gradient descent (SGD) as its learning algorithm. However, owing to its serial mechanism, an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial problems. Aiming at addressing this issue, this study proposes a momentum-incorporated parallel stochastic gradient descent (MPSGD) algorithm, whose main idea is two-fold: a) implementing parallelization via a novel data-splitting strategy, and b) accelerating convergence rate by integrating momentum effects into its training process. With it, an MPSGD-based latent factor (MLF) model is achieved, which is capable of performing efficient and high-quality recommendations. Experimental results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm, an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.
- Big data,
- industrial application,
- industrial data,
- latent factor analysis,
- machine learning,
- parallel algorithm,
- recommender system (RS),
- stochastic gradient descent (SGD)

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Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JAS.2020.1003396

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Proposing an MPSGD algorithm with faster convergence than existing parallel SGD algorithms when building an LF model for an RS.
Performing theoretical analysis and algorithm design for the proposed MPSGD-based LF model.
Conducting empirical studies on four HiDS matrices generated by industrial applications to evaluate the proposed model.

Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

doi: 10.1109/JAS.2020.1003396

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