OPAM：使用机器学习的在线购买行为分析
OPAM: Online Purchasing-behavior Analysis using Machine learning
摘要：
客户购买行为分析在开发在线供应商和客户之间有见地的沟通策略中起着关键作用。为了支持最近增加的在线购物的趋势，在这项工作中，我们提出了一个客户购买行为分析系统，使用监督，无监督和半监督学习方法。所提出的系统分析会话和用户旅程级别的购买行为，以确定客户类别/集群，这可能是有用的有针对性的消费者洞察规模。我们观察到更高的敏感性，在线购物门户网站的设计，会话级购买预测的准确率/召回率在91-98%/73- 99%，分别。用户旅程水平分析展示了五个独特的用户集群，其中“新购物者”是最可预测的，“冲动购物者”是最独特的低观看和高购物行为。此外，集群转换度量和部分标签学习证明了每个用户集群对新的/未标记的事件的鲁棒性。因此，客户群可以帮助战略目标的推动模型。
英文摘要：
Customer purchasing behavior analysis plays a key role in developing insightful communication strategies between online vendors and their customers. To support the recent increase in online shopping trends, in this work, we present a customer purchasing behavior analysis system using supervised, unsupervised and semi-supervised learning methods. The proposed system analyzes session and user-journey level purchasing behaviors to identify customer categories/clusters that can be useful for targeted consumer insights at scale. We observe higher sensitivity to the design of online shopping portals for session-level purchasing prediction with accuracy/recall in range 91-98%/73-99%, respectively. The user-journey level analysis demonstrates five unique user clusters, wherein 'New Shoppers' are most predictable and 'Impulsive Shoppers' are most unique with low viewing and high carting behaviors for purchases. Further, cluster transformation metrics and partial label learning demonstrates the robustness of each user cluster to new/unlabelled events. Thus, customer clusters can aid strategic targeted nudge models.
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。
