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Article citations


Shachter, R.D., DAmbrosio, B. and Del Favero, B.A. (1990) Symbolic Probabilistic Inference in Belief Networks. Proceedings of the 8th National Conference on Artificial Intelligence. Boston, Massachusetts, 29 July-3 August 1990, 2, 126-131.

has been cited by the following article:

  • TITLE: Bayesian Network Model of Product Information Diffusion and Reasoning of Influence

    AUTHORS: Xuehua Sun, Shaojie Hou, Ning Cai, Wenxiu Ma, Surui Zhao

    KEYWORDS: Product Information Diffusion, Bayesian Network Model, Influence Reasoning, Consumer Behaviors, Clique Tree

    JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.8 No.4, October 30, 2020

    ABSTRACT: Information diffusion on social media has become a key strategy in people’s daily interactions. This paper studies consumers’ participation in the product information diffusion, and analyzes the complexity of information diffusion which is affected by many factors. Prior investigations of information diffusion have primarily focused on the composition of diffusion networks with independent factors and the intricacy of the process has not been completely evaluated. The majority of prior investigations have focused on strategies and the moving forces in social media processes and the determination of influential seed nodes, with few evaluations conducted about the factors affecting consumers’ choices in information diffusion. In this study, a Bayesian network model of product information diffusion was created to examine the links between factors and consumer deportment. It revealed how those factors had an impact on each other and on consumer deportment choice. The innovation of the thesis is reflected in the exploration and analysis of the specific communication path of product information diffusion, which provides a better marketing idea and practical method for the development of mobile e-commerce. The research findings can help identify the quantitative relationships between the factors affecting the process of product information diffusion and user behavior.