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A hierarchical contextual attention-based network for sequential recommendation

文献类型：期刊论文

作者Cui, Qiang; Wu, Shu1; Huang, Yan; Wang, Liang
刊名NEUROCOMPUTING
出版日期2019-09-17
卷号358页码:141-149
关键词Sequential recommendation Recurrent neural network Short-term interest Context Attention mechanism
ISSN号0925-2312
DOI10.1016/j.neucom.2019.04.073
通讯作者Wu, Shu(shu.wu@nlpr.ia.ac.cn)
英文摘要The 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.
资助项目National Natural Science Foundation of China[61772528] ; National Natural Science Foundation of China[61871378] ; National Key Research and Development Program of China[2016YFB1001000]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000470106400012
出版者ELSEVIER SCIENCE BV
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/23691]  
专题自动化研究所_智能感知与计算研究中心
通讯作者Wu, Shu
作者单位1.Chinese Acad Sci CASIA, NLPR, CRIPAC, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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