Papers with EQR

2 papers
Identifying High Consideration E-Commerce Search Queries (2024.emnlp-industry)

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Challenge: Identifying high consideration queries is essential for e-commerce sites to better serve user needs . ecommerce sites can create or serve customized content for specific queries .
Approach: They propose an engagement-based Query Ranking approach to identify potential engagement levels with query-related shopping knowledge content during product search.
Outcome: The proposed method outperforms human-selected queries in terms of customer impact . human evaluation shows a precision of 96% for HC queries identified by the model .
Beyond Demonstrations: Dynamic Vector Construction from Latent Representations (2025.emnlp-main)

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Challenge: Existing methods for In-Context Learning (ICL) are sensitive to ICL-specific factors and rely on heuristic-based injection positions.
Approach: They propose a method that extracts task-relevant representations from large language models and reinjects them during inference.
Outcome: The proposed method outperforms few-shot In-Context Learning (ICL) and LoRA methods without repeated demonstration processing.

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