从化妆品到电子产品的网上购物行为分类：一个分析框架
Categorizing Online Shopping Behavior from Cosmetics to Electronics: An Analytical Framework
摘要：
在数字营销时代，现代公司的一个成功因素是了解客户如何根据他们的在线购物模式思考和行为。虽然通过问卷和调查收集消费者见解的传统方法仍然是市场情报部门描述性分析的基础，但我们提出了一个机器学习框架来自动化这一过程。在本文中，我们提出了一个模块化的消费者数据分析平台，处理用户和产品之间的会话级别的交互记录，以预测会话级别，用户旅程级别和客户行为的特定模式导致购买事件。我们探索的计算框架，并提供两个大数据集的化妆品和消费电子产品的大小分别为2GB和15 GB的测试结果。该系统实现了97-99%的分类准确率和召回率的用户旅程级购买预测和分类购买行为分为5个集群与两个数据集的购买比例增加。因此，所提出的框架可扩展到其他大型电子商务数据集，以获得自动购买预测和描述性消费者见解。
英文摘要：
A success factor for modern companies in the age of Digital Marketing is to understand how customers think and behave based on their online shopping patterns. While the conventional method of gathering consumer insights through questionnaires and surveys still form the bases of descriptive analytics for market intelligence units, we propose a machine learning framework to automate this process. In this paper we present a modular consumer data analysis platform that processes session level interaction records between users and products to predict session level, user journey level and customer behavior specific patterns leading towards purchase events. We explore the computational framework and provide test results on two Big data sets-cosmetics and consumer electronics of size 2GB and 15GB, respectively. The proposed system achieves 97-99% classification accuracy and recall for user-journey level purchase predictions and categorizes buying behavior into 5 clusters with increasing purchase ratios for both data sets. Thus, the proposed framework is extendable to other large e-commerce data sets to obtain automated purchase predictions and descriptive consumer insights.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
