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A hierarchical contextual attention-based network for sequential recommendation
Cui, Qiang; Wu, Shu1; Huang, Yan; Wang, Liang
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2019-09-17
Volume358Pages:141-149
Corresponding AuthorWu, Shu(shu.wu@nlpr.ia.ac.cn)
AbstractThe sequential recommendation is one of the most fundamental tasks for Web applications. Recently, recurrent neural network (RNN) based methods become popular and show effectiveness in many sequential recommendation tasks, such as next-basket recommendation and location prediction. The last hidden state of RNN is usually applied as the sequence's representation to make recommendations. RNN can capture the long-term interest with the help of gated activations or regularizers but has difficulty in acquiring the short-term interest due to the ordered modeling. In this work, we aim to strengthen the short-term interest, because it is beneficial to generate responsive recommendation according to recent behaviors. Accordingly, we propose a Hierarchical Contextual Attention-based (HCA) network. First, RNN is extended to model several adjacent factors at each time step. Such kind of multiple factors can be considered as a context where the short-term interest comes from. Then, within the context, the attention mechanism is used to find the important items that contribute to the short-term interest. This contextual attention-based technique is conducted on the input and hidden state of RNN respectively. In this way, we can relieve the limitation of ordered modeling of RNN, model the complicated correlations among recent factors, and strengthen the short-term interest. Experiments on two real-world datasets show that HCA can effectively generate the personalized ranking list and achieve considerable improvements. (C) 2019 Published by Elsevier B.V.
KeywordSequential recommendation Recurrent neural network Short-term interest Context Attention mechanism
DOI10.1016/j.neucom.2019.04.073
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61772528] ; National Natural Science Foundation of China[61871378] ; National Key Research and Development Program of China[2016YFB1001000]
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Program of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000470106400012
PublisherELSEVIER SCIENCE BV
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23691
Collection智能感知与计算研究中心
Corresponding AuthorWu, Shu
Affiliation1.Chinese Acad Sci CASIA, NLPR, CRIPAC, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Cui, Qiang,Wu, Shu,Huang, Yan,et al. A hierarchical contextual attention-based network for sequential recommendation[J]. NEUROCOMPUTING,2019,358:141-149.
APA Cui, Qiang,Wu, Shu,Huang, Yan,&Wang, Liang.(2019).A hierarchical contextual attention-based network for sequential recommendation.NEUROCOMPUTING,358,141-149.
MLA Cui, Qiang,et al."A hierarchical contextual attention-based network for sequential recommendation".NEUROCOMPUTING 358(2019):141-149.
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