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Detecting Product Adoption Intentions via Multiview Deep Learning

文献类型：期刊论文

作者Zhang, Zhu1,4; Wei, Xuan3; Zheng, Xiaolong1,2,4; Li, Qiudan1,4; Zeng, Daniel Dajun1,2,4
刊名INFORMS JOURNAL ON COMPUTING
出版日期2021-09-14
页码17
ISSN号1091-9856
关键词web mining business intelligence intention detection deep learning social media analytics
DOI10.1287/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.
WOS关键词SEARCH
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
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.Chinese Acad Sci, Inst Automat, State Key Lab ofManagement & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
3.Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Dept Informat Technol & Innovat, Shanghai 200030, 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.

入库方式： OAI收割

来源：自动化研究所

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