SemantiClean makes behavioral inference more auditable for e-commerce AI

A new arXiv paper introduces SemantiClean, a framework for extracting structured behavioral signals from e-commerce session data.

Instead of optimizing only for predictive accuracy, the framework is designed around auditability, governance, and reproducibility. It uses a shared element library to support inference targets such as purchase intent, customer segmentation, and product affinity.

That makes it relevant for applied AI teams that need explainable decision trails, especially in commercial settings where opaque behavioral inference can create trust and compliance problems.