SemantiClean: A Framework for Transparent, Auditable E-Commerce Behavioral Inference
Researchers have introduced SemantiClean, a modular framework that extracts structured behavioral signals from e-commerce session data to infer purchase intent, customer segmentation, and product affinity. The system deliberately prioritizes auditability and reproducibility over raw predictive accuracy, organizing 24 behavioral elements into a four-layer architecture with built-in anti-inflation safeguards. This matters because it addresses a growing demand for explainable, defensible AI decision-making in commercial settings where black-box predictions carry regulatory and ethical risks.
SemantiClean is a newly proposed framework designed to derive interpretable semantic signals from e-commerce browsing sessions, targeting inference tasks such as purchase intent, customer segmentation, and product affinity. Rather than optimizing purely for predictive accuracy as conventional end-to-end models do, the system explicitly trades marginal performance gains for transparency, structural governance, and deterministic reproducibility (sigma=0). The framework is built on the publicly available Online Shoppers Purchasing Intention (OSPI) dataset and organizes 24 behavioral elements across four architectural layers: Functional, Interaction, Systemic, and Contextual. To prevent signal distortion, three anti-inflation mechanisms are employed: RedundancyGroup contribution caps, a TieredPenaltyCalculator for bias penalties, and an AdaptiveConstraintMode for cold-start scenarios. A key component is the LLM-Integrated Semantic Inference Engine, a two-phase large language model-driven architecture that uses complete element metadata at inference time; while deterministic outputs are fully reproducible, two elements relying on LLM outputs carry controlled variability under fixed model and temperature settings. Notably, a planned gender inference target remains non-functional and is excluded from all reported results.
What's missing
The paper does not report standard benchmark comparisons against competing explainable AI or interpretable machine learning baselines, making it difficult to assess the magnitude of the accuracy trade-off accepted in exchange for auditability. Additionally, the paper does not discuss how SemantiClean would generalize to other e-commerce domains or datasets beyond OSPI.
What different sources said
- arXiv cs.AICenter
From Explicit Elements to Implicit Intent: A Predefined Library for Auditable Behavioral Inference
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