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A Dual-Attention Autoencoder Network for Efficient Recommendation System

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成果类型:
期刊论文
作者:
Duan, Chao;Sun, Jianwen;Li, Kaiqi;Li, Qing
通讯作者:
Qing Li
作者机构:
[Duan, Chao] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
[Li, Kaiqi; Li, Qing; Sun, Jianwen] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构:
[Qing Li] N
National Engineering Laboratory for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
语种:
英文
关键词:
recommendation system;matrix factorization;attention mechanism;autoencoder;collaborative filtering
期刊:
Electronics
ISSN:
2079-9292
年:
2021
卷:
10
期:
13
页码:
1581-
基金类别:
Conceptualization, Q.L., C.D.; methodology, Q.L.; software, C.D., K.L.; validation, C.D., J.S. and Q.L.; formal analysis, J.S., Q.L.; investigation, C.D.; resources, J.S., K.L.; data curation, J.S., K.L.; writing—original draft preparation, C.D.; writing—review and editing, Q.L.; visualization, C.D.; supervision, J.S.; project administration, Q.L.; funding acquisition, J.S., Q.L. All authors have read and agreed to the published version of the manuscript. This paper financially is supported by the National Natural Science Foundation of China (61807012, 62077021, 61807013), the Humanity and Social Science Youth Foundation of Ministry of Education of China (20YJC880083), the Strategic research projects of Ministry of Education of China (2020JYKX04), and the Fundamental Research Funds for the Central Universities (CCNU20QN027,CCNU20ZN007).
机构署名:
本校为第一机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Accelerated development of mobile networks and applications leads to the exponential expansion of resources, which causes problems such as trek and overload of information. One of the practical approaches to ease these problems is recommendation systems (RSs) that can provide individualized service. Video recommendation is one of the most critical recommendation services. However, achieving satisfactory recommendation service on the sparse data is difficult for video recommendation service. Moreover, the cold start problem further exacerbates the research challenge. Recent state-of-the-art wor...

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