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This paper applies a two-stage XGBoost model to optimize signal placement on eBay's View-Item pages, leveraging counterfactual estimation to improve conversion rates for high-value items. While the study falls under Artificial Intelligence (cs.AI) and Information Retrieval (cs.IR), its primary focus is on commercial recommendation systems rather than fundamental AI research or quantum advancements.
Stageboost: Recommending Signals Based on Counterfactual Estimation
arXiv:2608.27366v1 Announce Type: cross Abstract: Signals are short textual or visual snippets displayed on the eBay View-Item (VI) page, providing additional, contextual information for users about the viewed item. The aim of displaying these signals is to facilitate intelligent purchase and to incentivize engagement. In this paper, we present a 2 stage xgboost based model that optimally populates the VI page with signals. This approach has shown a 0.08% lift in overall GMB (Gross Merchandise Bought) and 0.58% increase in Parts and Accessories GMB, primarily due to increase in conversion of high average price items in online experimentation.
Abstract:Signals are short textual or visual snippets displayed on the eBay View-Item (VI) page, providing additional, contextual information for users about the viewed item. The aim of displaying these signals is to facilitate intelligent purchase and to incentivize engagement. In this paper, we present a 2 stage xgboost based model that optimally populates the VI page with signals. This approach has shown a 0.08% lift in overall GMB (Gross Merchandise Bought) and 0.58% increase in Parts and Accessories GMB, primarily due to increase in conversion of high average price items in online experimentation.
Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
arXiv:2608.27366 [cs.IR]
(or arXiv:2608.27366v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2608.27366 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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