Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning
文献类型：期刊论文
| 作者 | Luo, Xin1,8 ; Qin, Wen2,3; Dong, Ani4; Sedraoui, Khaled5,6; Zhou, MengChu6,7
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| 刊名 | IEEE-CAA JOURNAL OF AUTOMATICA SINICA
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| 出版日期 | 2021-02-01 |
| 卷号 | 8期号:2页码:402-411 |
| ISSN号 | 2329-9266 |
| 关键词 | Big data industrial application industrial data latent factor analysis machine learning parallel algorithm recommender system (RS) stochastic gradient descent (SGD) |
| DOI | 10.1109/JAS.2020.1003396 |
| 通讯作者 | Zhou, MengChu(zhou@njit.edu) |
| 英文摘要 | 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 datasplitting 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. |
| 资助项目 | National Natural Science Foundation of China[61772493] ; King Abdulaziz University[RG-48-135-40] ; Guangdong Province Universities and College Pearl River Scholar Funded Scheme (2019) ; Natural Science Foundation of Chongqing[cstc2019jcyjjqX0013] |
| WOS研究方向 | Automation & Control Systems |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000607401900008 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/12834]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Zhou, MengChu |
| 作者单位 | 1.Dongguan Univ Technol, Sch Comp Sci & Technol, Dongguan 523808, Peoples R China 2.Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China 3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China 4.Dongguan Univ Technol, City Coll, Dept Comp & Informat Sci, Dongguan 523419, Peoples R China 5.King Abdulaziz Univ, Dept Elect & Comp Engn, Fac Engn, Jeddah 21481, Saudi Arabia 6.King Abdulaziz Univ, Ctr Res Excellence Renewable Energy & Power Syst, Jeddah 21481, Saudi Arabia 7.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA 8.Cloudwalk, Dept Big Data Anal Tech, Hengrui Chongqing Artificial Intelligence Res Ctr, Chongqing 401331, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, Xin,Qin, Wen,Dong, Ani,et al. Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning[J]. IEEE-CAA JOURNAL OF AUTOMATICA SINICA,2021,8(2):402-411. |
| APA | Luo, Xin,Qin, Wen,Dong, Ani,Sedraoui, Khaled,&Zhou, MengChu.(2021).Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning.IEEE-CAA JOURNAL OF AUTOMATICA SINICA,8(2),402-411. |
| MLA | Luo, Xin,et al."Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning".IEEE-CAA JOURNAL OF AUTOMATICA SINICA 8.2(2021):402-411. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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