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EAGER framework enhances e-commerce query recommendations using LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EAGER, a novel two-stage framework designed to generate more relevant query suggestions for e-commerce search. The first stage, enrichment, uses supervised fine-tuning with a curriculum that progressively increases information richness and reasoning depth, incorporating rationale augmentation and diversity regularization. The second stage, alignment, employs GRPO training with a hybrid reward system combining business signals and click-based preferences. EAGER has demonstrated significant effectiveness in both offline experiments and online A/B testing, and has already been deployed in a production environment by a major e-commerce platform. AI

IMPACT This framework could significantly improve the relevance and personalization of search results in e-commerce, leading to better user experience and conversion rates.

RANK_REASON This is a research paper detailing a new framework for query recommendation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

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

EAGER framework enhances e-commerce query recommendations using LLMs

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This is a research paper detailing a new framework for query recommendation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xiaoyi Zeng ·

    EAGER: Enrich-and-Align Generative Query Recommendation from Clicked Items in E-commerce Search

    E-commerce platforms increasingly display clickable query suggestions alongside items in the user feed, enabling users to refine or expand their intent without manually reformulating queries. Existing approaches either mine suggestions from historical logs -- limited to past beha…

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