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ML pipeline maps noisy retail product names to price categories

By PulseAugur Editorial · [2 sources] ·

A new research paper proposes a machine learning pipeline to categorize retail product names into consumer-price categories. The method involves text normalization, a rule-based classifier using key phrases, and a binary confirmation model. It also incorporates a human-in-the-loop labeling protocol with reliability weighting for continuous fine-tuning. AI

IMPACT Provides a scalable method for classifying unstructured product data, potentially improving price index accuracy and market analysis.

RANK_REASON The cluster contains an academic paper detailing a novel machine learning methodology.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ML pipeline maps noisy retail product names to price categories

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The cluster contains an academic paper detailing a novel machine learning methodology.
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paper, product
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125 days old
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Vladimir Beskorovainyi ·

    Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling

    arXiv:2606.02004v1 Announce Type: new Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data. A recurring obstacle is that product descriptions in such sources are short, noisy, and abbreviated, wi…

  2. arXiv cs.CL TIER_1 English(EN) · Vladimir Beskorovainyi ·

    Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling

    Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data. A recurring obstacle is that product descriptions in such sources are short, noisy, and abbreviated, with no standard product code, so each item must f…

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