The paper presents an adaptive browserless system for extracting prices from e‑commerce websites, improving robustness to structural differences. It combines HTML fragmentation with syntactic, semantic, and frequency rules, then enhances the baseline with a Bayesian weight‑update and a genetic‑algorithm parameter optimizer. The hybrid approach raises precision from 77.2% to 87.3% and cuts per‑page processing time by about 14%, positioning it as a competitive, low‑cost alternative to manual wrappers, browser‑based, or LLM‑driven methods.
arXiv:2606. 13741v1 Announce Type: new Abstract: This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce.
arXiv:2606. 14821v1 Announce Type: cross Abstract: The abundant and heterogeneous nature of web content necessitates automated information extraction, and generating scrapers that can be reused across similar web pages offers an effective solution for scalable data extraction.
The paper introduces LLP, a Large Language Model–based generative framework for pricing second‑hand products on consumer‑to‑consumer platforms. LLP retrieves similar items to capture market dynamics, then uses LLMs to generate price suggestions, refined through supervised fine‑tuning and group relative policy optimization. A confidence‑based filter rejects unreliable predictions, and experiments show LLP outperforms prior methods, achieving higher static adoption rates when deployed on Xianyu.
arXiv:2607. 03886v1 Announce Type: cross Abstract: Sponsored search plays a crucial role as a revenue stream for search engines, wherein advertisers competitively bid on keywords that align with the users' search queries.
arXiv:2608.29850v1 Announce Type: new Abstract: E-commerce platforms increasingly personalize user experiences through machine learning, yet page layout decisions remain dominated by static rules and...
arXiv:2606. 26787v1 Announce Type: cross Abstract: Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and misalignment with long-term business objectives such as cumulative Gross Merchandise Value (GMV), Return on Investment (ROI) and milestone achievement.
arXiv:2606. 02004v1 Announce Type: cross Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data.
arXiv:2607. 12056v1 Announce Type: new Abstract: Online shopping is increasingly shifting toward a model in which AI agents independently search for products, compare options, evaluate constraints, and carry out parts of the purchasing process for users.
The paper extends the Expected Cost-per-Mille (eCPM) framework to handle auction and hybrid "Auction with Buy It Now" (ABIN) listings by deriving a marginal eCPM (meCPM) that captures the incremental value of showing an additional impression for items whose prices evolve dynamically. This unified ranking objective allows fixed-price, auction, and ABIN listings to be compared and ranked together. A production implementation was tested via online A/B experiments on a large e-commerce platform, yielding positive revenue gains and statistically significant improvements in user metrics, leading to deployment in production.
Web agents that act from rendered pixels avoid the fragility and heavy token cost of reading a page's HTML or accessibility tree, but training them depends on large amounts of high-quality interaction...
arXiv:2608. 12674v1 Announce Type: new Abstract: Maintaining price consistency and executing an Every Day Low Price strategy is critical for global retailers.
arXiv:2607. 08785v1 Announce Type: cross Abstract: High-quality data drives machine learning advances across industries.