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Abstract: We study the problem of business model mining and prediction in the e-commerce context. Unlike most existing approaches where this is typically formulated as a regression problem or a time-series prediction problem, we take a different formulation to this problem by noting that these existing approaches fail to consider the potential relationships both among the consumers (consumer influence) and among the shops (competitions or collaborations). Taking this observation into consideration, we propose a new method for e-commerce business model mining and prediction, called EBMM, which combines regression with community analysis. The challenge is that the links in the network are typically not directly observed, which is addressed by applying information diffusion theory through the consumer-shop network. Extensive evaluations using Alibaba Group e-commerce data demonstrate the promise and superiority of EBMM to the state-of-the-art methods in terms of business model mining and prediction.

In this paper, the authors have studied the problem of business model mining and prediction in the e-commerce context. Unlike the most existing approaches in the literature where this problem is typically formulated as a regression problem or as a time-series prediction problem, the authors of this paper have developed a different formulation to this problem by noting that these existing approaches fail to consider the potential relationships existing both among the consumers (consumer influence) and among the shops (competitions or collaborations). Authors did an excellent innovation on the methodology.

应用于电子商务环境的商业模式挖掘和预测方法

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