| A hierarchical contextual attention-based network for sequential recommendation | |
Cui, Qiang ; Wu, Shu1; Huang, Yan; Wang, Liang
| |
| Source Publication | NEUROCOMPUTING
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| ISSN | 0925-2312 |
| 2019-09-17 | |
| Volume | 358Pages:141-149 |
| Corresponding Author | Wu, Shu(shu.wu@nlpr.ia.ac.cn) |
| Abstract | 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. |
| Keyword | Sequential recommendation Recurrent neural network Short-term interest Context Attention mechanism |
| DOI | 10.1016/j.neucom.2019.04.073 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[61772528] ; National Natural Science Foundation of China[61871378] ; National Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[61772528] ; National Natural Science Foundation of China[61871378] ; National Key Research and Development Program of China[2016YFB1001000] |
| Funding Organization | National Natural Science Foundation of China ; National Key Research and Development Program of China |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence |
| WOS ID | WOS:000470106400012 |
| Publisher | ELSEVIER SCIENCE BV |
| Sub direction classification | 推荐系统 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/23691 |
| Collection | 智能感知与计算 |
| Corresponding Author | Wu, Shu |
| Affiliation | 1.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 Affilication | Chinese 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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| HCA Neuro_ revised P（545KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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