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Predicting product adoption intentions: An integrated behavioral model-inspired multiview learning approach
Authors:Zhu Zhang  Xuan Wei  Xiaolong Zheng  Daniel Dajun Zeng
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
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.
Keywords:
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