文献
J-GLOBAL ID:202202292364676760
整理番号:22A0960370
レコメンダーシステムのための分散代替確率勾配降下による大規模でスケーラブルな潜在因子分析【JST・京大機械翻訳】
Large-Scale and Scalable Latent Factor Analysis via Distributed Alternative Stochastic Gradient Descent for Recommender Systems
著者 (5件):
Shi Xiaoyu
(Chongqing Engineering Research Center of Big Data Application for Smart Cities, and Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China)
,
He Qiang
(School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia)
,
Luo Xin
(Chongqing Engineering Research Center of Big Data Application for Smart Cities, and Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China)
,
Bai Yanan
(Chongqing Engineering Research Center of Big Data Application for Smart Cities, and Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China)
,
Shang Mingsheng
(Chongqing Engineering Research Center of Big Data Application for Smart Cities, and Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China)
資料名:
IEEE Transactions on Big Data
(IEEE Transactions on Big Data)
巻:
8
号:
2
ページ:
420-431
発行年:
2022年
JST資料番号:
W2442A
ISSN:
2332-7790
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
アメリカ合衆国 (USA)
言語:
英語 (EN)