arXiv AI
LLM-based User-Product Relevance Evaluation for Dynamic Product Advertising
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SMART: LLM-Augmented Hybrid Retrieval for Dynamic Product Ads
arXiv:2607. 23121v1 Announce Type: cross Abstract: Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and prospecting (discovering new categories).
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Improving Item Discoverability in e-Commerce Search via Related Intent Generation
arXiv:2607. 27172v1 Announce Type: cross Abstract: Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall.
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Improving Item Discoverability in e-Commerce Search via Related Intent Generation
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Enhancement of E-commerce Sponsored Search Relevancy with LLM
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.
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Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments
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Mine and Refine: Optimizing Graded Relevance in E-commerce Semantic Search Retrieval
The paper introduces Mine and Refine, a two‑stage contrastive training framework designed to improve embedding‑based retrieval for large‑scale e‑commerce search. It tackles graded relevance, hard‑sample mining, and unstable similarity separability by using a lightweight LLM as a scalable labeler and a multi‑level circle loss to enforce margin‑controlled separation across relevance levels. The method has been deployed in production across multiple product verticals, yielding statistically significant increases in user engagement, gross order value, and retrieval relevance metrics.