arXiv:2606.01504v1 Announce Type: cross Abstract: Semantic retrieval in e-commerce must handle short, noisy, and colloquial queries over large product catalogs with fine-grained attribute distinctions. We present a Siamese LLM dual-encoder trained through a two-stage pipeline: contrastive learning with a false-negative margin mask to prevent penalization of near-duplicate products, followed by Relative Odds Alignment for Retrieval (ROAR), a preference optimization objective that extends Bradley-Terry to variable-sized graded relevance groups via consecutive odds-ratio margins. The training cor
The proliferation of LLMs and advanced machine learning techniques has reached a point where their application to complex, real-world e-commerce search problems is becoming increasingly viable and effective.
Improving semantic retrieval in e-commerce can significantly enhance user experience, boost conversion rates, and better leverage the vast inventory data of online retailers, creating a competitive advantage.
Product search will become more intuitive and accurate, moving beyond keyword matching to understanding user intent and product attributes, reducing friction for consumers and increasing sales for businesses.
- · E-commerce platforms
- · Online retailers
- · AI/ML companies specializing in search
- · Consumers
- · E-commerce platforms with outdated search tech
- · Keyword-stuffing SEO specialists
More efficient and personalized product discovery for online shoppers.
Increased market share for e-commerce platforms that successfully implement advanced semantic search, potentially leading to further consolidation.
New forms of product advertising and recommendation systems that are more deeply integrated with semantic understanding, allowing for highly targeted and effective campaigns.
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Read at arXiv cs.LG