Predicting product adoption intentions: An integrated behavioral model-inspired multiview learning approach |
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Authors: | Zhu Zhang Xuan Wei Xiaolong Zheng Daniel Dajun Zeng |
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Affiliation: | 1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;2. Shenzhen Artificial Intelligence and Data Science Research Institute (Longhua), Shenzhen 518129, China;3. Department of Information, Technology and Innovation, Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China;4. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China |
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Abstract: | Mining product adoption intentions from social media could provide insights for many business practices, such as social media marketing. Existing methods mainly focus on text information but overlook other types of data. In light of the Integrated Behavioral Model (IBM), in this study, we argue that it is valuable to consider users’ social connections in addition to postings for identifying product adoption intentions. Based on this rationale, we propose a novel multiview deep learning framework to identify product adoption intentions. Extensive experiments show our proposed approach is effective, and demonstrate the benefit of incorporating social network information for intention identification. |
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