From Explicit Elements to Implicit Intent: A Predefined Library for Auditable Behavioral Inference
Quick Answer
SemantiClean is a modular framework for extracting structured signals from e-commerce data, prioritizing auditability and reproducibility over mere accuracy.
Quick Take
It organizes behavioral elements into a four-layer architecture and employs anti-inflation mechanisms to ensure signal quality, with a fully implemented -Integrated Semantic Inference Engine for inference tasks.
Key Points
- SemantiClean uses a four-layer architecture for behavioral element organization.
- The framework includes anti-inflation mechanisms to maintain signal quality.
- LLM-Integrated Semantic Inference Engine provides reproducible outputs.
- Current implementation excludes gender inference from quantitative results.
- Framework built on the Online Shoppers Purchasing Intention dataset.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 11207v1 Announce Type: new Abstract: We present SemantiClean, a modular framework for extracting structured semantic signals from e-commerce session data and driving pluggable inference targets including purchase intent, customer segmentation, and product affinity through a shared element library.
Unlike conventional end-to-end predictors that optimise solely for accuracy, SemantiClean prioritises auditability, structural governance, and sigma=0 reproducibility, explicitly trading marginal predictive gains for element-level transparency and defensible decision trails.
Built upon the Online Shoppers Purchasing Intention (OSPI) dataset, the framework organises twenty-four behavioural elements into a four-layer architecture (Functional, Interaction, Systemic, Contextual) and enforces signal quality through three anti-inflation mechanisms: RedundancyGroup contribution caps, TieredPenaltyCalculator bias penalties, and AdaptiveConstraintMode cold-start protection.
This report introduces the -Integrated Semantic Inference Engine, a fully implemented two-phase LLM-driven inference architecture that leverages complete element metadata at inference time. All quantitative results reported herein are produced by this engine. Deterministic engine outputs remain fully reproducible (sigma=0); LLM-dependent results (E8, E10) are subject to controlled output variability under fixed provider/model/temperature settings.
The gender inference target remains non-functional in the current implementation and is excluded from all quantitative results.
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