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New adaptive browserless system improves web price extraction accuracy

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces an adaptive browserless system for extracting product prices from e-commerce websites. This hybrid approach combines HTML fragmentation with syntactic, semantic, and frequency rules, enhanced by a Bayesian method for dynamic rule weighting and a genetic algorithm for parameter optimization. The system demonstrated improved precision from 77.2% to 87.3% and reduced processing time by approximately 14% compared to a baseline, offering a cost-effective and accurate alternative to browser-based or LLM-based extraction methods. AI

IMPACT This research offers a more efficient and accurate method for e-commerce data extraction, potentially improving market monitoring and price comparison tools.

RANK_REASON The cluster contains a research paper detailing a new method for web price extraction. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New adaptive browserless system improves web price extraction accuracy

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The cluster contains a research paper detailing a new method for web price extraction. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Evgeniia Kositsyna, Jorge Lloret-Gazo ·

    Web Price Extraction: State of the Art and an Adaptive Browserless Implementation

    arXiv:2609.01030v1 Announce Type: cross Abstract: Price extraction from websites is a key task for market monitoring, price comparison, and business analytics in e-commerce. Existing approaches can be broadly divided into four groups, and understanding their trade-offs in accurac…

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