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A Neural Network Approach to Support B2B Negotiations

机译:支持B2B谈判的神经网络方法

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摘要

In this paper, an intelligent approach for B2B electronic commerce to help businesses choose their negotiating parties is presented. The premise is that the participants are often less concerned with price and more with relationships in B2B commerce. Our approach, as proposed in this paper, consists of two main steps. First, a social network analysis method is used to mine the participant relationships on market space automatically. Second, a neural network method is used to recommend the most suitable trading partners to a user in the online marketplace before implementing actual negotiations. Relationships mined in the first step will be used as important parameters in the proposed neural network approach. The resulted neural network model is used by each participant to estimate the negotiation results with its potential negotiation partners. The intelligent approach is tested on the B2B platform we developed. It is quite useful to diminish the digital gaps among the participants and save energies in actual negotiation by using the estimated negotiation results. In addition, it is a helpful supporting tool for newcomers of the platform to enter the market. Furthermore, we provide an interface for users to set their preferred weights of relationships, prices, and the cost of negotiations, and applying their preferences to the trading partner selection in actual negotiations.
机译:本文提出了一种B2B电子商务的智能方法,以帮助企业选择谈判方。前提是参与者通常不太关心价格,而更关心B2B贸易中的关系。如本文所提出的,我们的方法包括两个主要步骤。首先,使用社交网络分析方法自动挖掘市场空间上的参与者关系。其次,在实施实际谈判之前,使用神经网络方法向在线市场中的用户推荐最合适的贸易伙伴。第一步中挖掘的关系将在拟议的神经网络方法中用作重要参数。每个参与者都使用所得的神经网络模型来估计与其潜在谈判伙伴的谈判结果。该智能方法已在我们开发的B2B平台上进行了测试。通过使用估计的协商结果来减小参与者之间的数字鸿沟并节省实际协商中的能量是非常有用的。此外,它是该平台新手进入市场的有用的支持工具。此外,我们为用户提供了一个界面,用于设置他们的首选关系权重,价格和谈判成本,并将他们的偏好应用于实际谈判中的贸易伙伴选择。

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