SemantiClean: A Framework for Transparent, Auditable E-Commerce Behavioral Inference
Researchers have developed SemantiClean, a modular framework that extracts semantic signals from e-commerce session data to predict purchase intent, customer segments, and product preferences while prioritizing auditability over raw accuracy. The framework organizes 24 behavioral elements into four architectural layers and uses three mechanisms to prevent signal inflation and bias. The approach represents a shift toward explainable AI in commercial applications, trading some predictive performance for transparency and reproducibility.
SemantiClean is a new framework designed to infer customer behavior from e-commerce session data in a way that prioritizes transparency and auditability over maximum predictive accuracy. The system organizes 24 behavioral elements into a four-layer architecture (Functional, Interaction, Systemic, Contextual) and uses three anti-inflation mechanisms—RedundancyGroup contribution caps, TieredPenaltyCalculator bias penalties, and AdaptiveConstraintMode cold-start protection—to maintain signal quality and prevent overfitting. Built on the Online Shoppers Purchasing Intention (OSPI) dataset, the framework supports multiple inference targets including purchase intent prediction, customer segmentation, and product affinity modeling. The researchers implemented an LLM-integrated inference engine with two phases that maintains deterministic reproducibility (sigma=0) for standard outputs, though LLM-dependent results show controlled variability. The authors acknowledge that the gender inference target remains non-functional in the current implementation.
What's missing
The paper does not provide comparative performance metrics against conventional end-to-end predictors, making it unclear how much predictive accuracy is sacrificed for auditability. Additionally, the specific quantitative results and benchmarks are not detailed in the abstract, and the practical applicability of the framework to real-world e-commerce systems beyond the OSPI dataset is not discussed.
What different sources said
- arXiv cs.AICenter
From Explicit Elements to Implicit Intent: A Predefined Library for Auditable Behavioral Inference
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