Detecting Product Adoption Intentions via Multiview Deep Learning | |
Zhang, Zhu2,4; Wei, Xuan1; Zheng, Xiaolong2,3,4; Li, Qiudan2,4; Zeng, Daniel Dajun2,3,4 | |
刊名 | INFORMS JOURNAL ON COMPUTING |
2021-09-14 | |
页码 | 17 |
关键词 | web mining business intelligence intention detection deep learning social media analytics |
ISSN号 | 1091-9856 |
DOI | 10.1287/ijoc.2021.1083 |
通讯作者 | Zheng, Xiaolong(xiaolong.zheng@ia.ac.cn) |
英文摘要 | Detecting product adoption intentions on social media could yield significant value in a wide range of applications, such as personalized recommendations and targeted marketing. In the literature, no study has explored the detection of product adoption intentions on social media, and only a few relevant studies have focused on purchase intention detection for products in one or several categories. Focusing on a product category rather than a specific product is too coarse-grained for precise advertising. Additionally, existing studies primarily focus on using one type of text representation in target social media posts, ignoring the major yet unexplored potential of fusing different text representations. In this paper, we first formulate the problem of product adoption intention mining and demonstrate the necessity of studying this problem and its practical value. To detect a product adoption intention for an individual product, we propose a novel and general multiview deep learning model that simultaneously taps into the capability of multiview learning in leveraging different representations and deep learning in learning latent data representations using a flexible nonlinear transformation. Specifically, the proposed model leverages three different text representations from a multiview perspective and takes advantage of local and long-term word relations by integrating convolutional neural network (CNN) and long short-term memory (LSTM) modules. Extensive experiments on three Twitter datasets demonstrate the effectiveness of the proposed multiview deep learning model compared with the existing benchmark methods. This study also significantly contributes research insights to the literature about intention mining and provides business value to relevant stakeholders such as product providers. |
资助项目 | Ministry of Science and Technology of China[2020AAA0108401] ; Ministry of Science and Technology of China[2019QY (Y) 0101] ; Ministry of Science and Technology of China[2020AAA0103405] ; Science Fund for Creative Research Groups of the National Natural Science Foundation of China[71621002] ; National Natural Science Foundation of China[71472175] ; National Natural Science Foundation of China[71974187] ; National Natural Science Foundation of China[61671450] ; National Natural Science Foundation of China[71902179] ; National Natural Science Foundation of China[72074209] ; National Natural Science Foundation of China[71825007] ; Strategic Priority Research Pro-gram of Chinese Academy of Sciences[XDA27030100] ; Research Foundation of SKL-MCCS for Young Scientists[20190212] ; Longhua District Science and Technology Innovation Fund[10162a20200617b70da63] ; National Science Foundation[1228509] |
WOS关键词 | SEARCH |
WOS研究方向 | Computer Science ; Operations Research & Management Science |
语种 | 英语 |
出版者 | INFORMS |
WOS记录号 | WOS:000708981400001 |
资助机构 | Ministry of Science and Technology of China ; Science Fund for Creative Research Groups of the National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Pro-gram of Chinese Academy of Sciences ; Research Foundation of SKL-MCCS for Young Scientists ; Longhua District Science and Technology Innovation Fund ; National Science Foundation |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/46230] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
通讯作者 | Zheng, Xiaolong |
作者单位 | 1.Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Dept Informat Technol & Innovat, Shanghai 200030, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab ofManagement & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 4.Shenzhen Artificial Intelligence & Data Sci Inst, Shenzhen 518129, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Zhu,Wei, Xuan,Zheng, Xiaolong,et al. Detecting Product Adoption Intentions via Multiview Deep Learning[J]. INFORMS JOURNAL ON COMPUTING,2021:17. |
APA | Zhang, Zhu,Wei, Xuan,Zheng, Xiaolong,Li, Qiudan,&Zeng, Daniel Dajun.(2021).Detecting Product Adoption Intentions via Multiview Deep Learning.INFORMS JOURNAL ON COMPUTING,17. |
MLA | Zhang, Zhu,et al."Detecting Product Adoption Intentions via Multiview Deep Learning".INFORMS JOURNAL ON COMPUTING (2021):17. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论