Evaluation of Analysis Model for Products with Coefficients of Binary Classifiers and Consideration of Way to Improve
Evaluation of Analysis Model for Products with Coefficients of Binary Classifiers and Consideration of Way to Improve
复制标题
二元分类器系数产品分析模型的评价及改进途径的思考
DOI：
10.1007/978-3-031-05064-0_29
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Masayuki Goto
中科院分区：
文献类型：
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作者：
Ayako Yamagiwa;Masayuki Goto
近年来，电子商务网站上的购买行为对普通消费者来说已经变得非常普遍。过去在线下商店购买的产品也被处理。这类产品，如礼品或耐用消费品，通常购买频率很低，每次购买时，其偏好的物品都会发生变化。为了提高顾客满意度，人们提出了许多方法来分析购买历史数据。但是，大多数研究都是着眼于顾客与产品之间的共现关系，将同一顾客购买的产品视为相似。然后，它是很难使用传统的产品分析方法，已经提出了购买历史数据是困难的前面提到的一些类型的数据，因此，作者提出了一种分析方法，提取产品的特征，通过使用的二元分类器的系数，区分产品购买与否。在这项研究中，我们进行实验，人工数据，以评估我们的方法。具体来说，我们验证如何准确的系数可以估计，在什么情况下，他们可以更准确地估计。
Purchasing actions on e-commerce sites have become very common for general consumers in recent years. Products that were used to be bought at offline shops are purchased are also handled. Such products, like gifts or durable consumer goods, are often purchased infrequently and whose prefer items change each time they are purchased. A lot of methods are proposed for analysis purchase history data in order to improve customer satisfaction. However, most of them focus on the co-occurrence relationship between customers and products and treat products purchased by the same customer as similar. Then, it is difficult to use the conventional product analysis methods that have been proposed for purchase history data is difficult for some kinds of data mentioned before.Therefore, the authors have proposed an analysis method with extracting features of products by using the coefficients of binary classifiers that discriminates product purchases or not. In this study, we conduct experiments with artificial data in order to evaluate our method. Specifically, we verify how accurately the coefficients can be estimated and under what circumstances they can be estimated more accurately.