From Explicit Elements to Implicit Intent: A Predefined Library for Auditable Behavioral Inference
Researchers have introduced SemantiClean, a novel framework designed to extract structured semantic signals from e-commerce session data. This system prioritizes auditability and reproducibility over marginal predictive gains, organizing behavioral elements into a four-layer architecture. The framework utilizes an LLM-Integrated Semantic Inference Engine to ensure deterministic and reproducible outputs, with a focus on transparency and defensible decision trails. AI
IMPACT Introduces a new approach to AI inference that prioritizes transparency and auditability in e-commerce applications.