From Explicit Elements to Implicit Intent: A Predefined Library for Auditable Behavioral Inference

Here’s the rephrased translation:

A framework called SemantiClean has been developed to extract structured semantic signals from e-commerce session data and direct inference objectives such as identifying customer purchasing intentions, segmenting customer groups, and determining product affinities. Unlike conventional predictors that focus on accuracy, SemantiClean prioritizes auditing, structural governance, and reproducibility, trading off marginal predictive gains for transparency at the element level and defensible decision trails. This framework is based on a dataset of online buyers’ purchasing intentions and organizes 24 behavioral elements into a four-layer architecture. Additionally, an integrated semantic inference engine has been introduced, which uses complete element metadata at the time of inference. This development is significant because it demonstrates how artificial intelligence can be used to improve understanding of customer behavior in e-commerce, potentially having substantial implications for the development of effective marketing and sales strategies. The ability to audit and reproduce inference results is also crucial for ensuring transparency and trust in artificial intelligence systems.

Read the original article on arXiv cs.AI

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