CASPR：基于客户活动序列的预测与表示
CASPR: Customer Activity Sequence-based Prediction and Representation
摘要：
对企业盈利能力至关重要的任务，如客户流失预测、欺诈账户检测或客户终身价值估计，通常由基于表格格式客户数据设计的特征训练的模型来处理。随着时间的推移，特定于应用程序的功能工程会增加开发、运营和维护成本。表示学习的最新进展为简化和推广跨应用程序的特征工程提供了机会。当将这些进步应用于表格数据时，研究人员需要处理数据异构性，客户参与历史的变化或企业数据集的庞大数量。在本文中，我们提出了一种新的方法来编码包含客户交易，购买历史和其他互动的表格数据到一个通用的表示客户的关联业务。然后，我们评估这些嵌入功能，训练多个模型跨越各种应用程序。CASPR，基于客户活动序列的预测和表示，应用Transformer架构对活动序列进行编码，以提高模型性能，避免跨应用程序的定制特征工程。我们的大规模实验验证了CASPR适用于小型和大型企业应用程序。
英文摘要：
Tasks critical to enterprise profitability, such as customer churn prediction, fraudulent account detection or customer lifetime value estimation, are often tackled by models trained on features engineered from customer data in tabular format. Application-specific feature engineering adds development, operationalization and maintenance costs over time. Recent advances in representation learning present an opportunity to simplify and generalize feature engineering across applications. When applying these advancements to tabular data researchers deal with data heterogeneity, variations in customer engagement history or the sheer volume of enterprise datasets. In this paper, we propose a novel approach to encode tabular data containing customer transactions, purchase history and other interactions into a generic representation of a customer's association with the business. We then evaluate these embeddings as features to train multiple models spanning a variety of applications. CASPR, Customer Activity Sequence-based Prediction and Representation, applies Transformer architecture to encode activity sequences to improve model performance and avoid bespoke feature engineering across applications. Our experiments at scale validate CASPR for both small and large enterprise applications.
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。
