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Application of Bayesian Network Sheds Light on Purchase Decision Process Basing on RFID Technology

机译:贝叶斯网络在基于RFID技术的采购决策过程中的应用

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Recently, a wireless non-contact technology named RFID(Radio Frequency Identification) has brought a new perspective on process of purchase decision. Via the RFID tag attached to a shopping cart, the position information of customers in a grocery store can be captured every moment. This paper presents our study based on this type of data. In this study, we transform the RFID data into stay time that the customers spend in supermarket until purchase is decided. In addition to RFID data, a purchase transaction based on POS(Point of Sale) data that only represent the point when customers come to purchase can be extended to a process of in-store behavior. As a probabilistic graphical model named bayesian network is employed to applied, we investigate the stay time how to affect the purchase probability. In the experiment, we also reveal that this affect is different during the purchase decision process by customers in individual age bracket. Moreover, numerical results show our proposal has a better accuracy than other models such as logistic regression analysis.
机译:最近,一种称为RFID(射频识别)的无线非接触式技术为购买决策过程带来了新的视角。通过附在购物车上的RFID标签,可以随时捕获顾客在杂货店中的位置信息。本文介绍了基于此类数据的研究。在这项研究中,我们将RFID数据转换为客户在超级市场花费的停留时间,直到决定购买为止。除了RFID数据外,基于POS(销售点)数据的购买交易也可以扩展到店内行为的整个过程,而POS数据仅代表客户购买时的购买点。由于应用了一个称为贝叶斯网络的概率图形模型,我们研究了停留时间如何影响购买概率。在实验中,我们还发现,在不同年龄段的客户进行购买决策过程中,这种影响是不同的。而且,数值结果表明我们的建议比其他模型(如逻辑回归分析)具有更好的准确性。

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