Papers with product retrieval

3 papers
Graph-based Multilingual Product Retrieval in E-Commerce Search (2021.naacl-industry)

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Challenge: Modern e-commerce search systems require product retrieval under multilingual scenarios.
Approach: They propose a universal multilingual retrieval system that captures interactions between search queries and items in e-commerce search.
Outcome: The proposed system outperforms state-of-the-art retrieval models on five countries and has been deployed in production for multiple countries.
Reinforcement Learning for Adversarial Query Generation to Enhance Relevance in Cold-Start Product Search (2025.acl-industry)

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Challenge: Existing methods do not incorporate feedback from the query relevance model, limiting their ability to generate queries that enhance product retrieval.
Approach: They propose an adversarial reinforcement learning framework that exposes weaknesses in query classification models by creating synthetic queries that augment the classifier's training set.
Outcome: The proposed framework improves query generation performance on public datasets and on proprietary datasets.
Pay Attention to Implicit Attribute Values: A Multi-modal Generative Framework for AVE Task (2023.findings-acl)

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Challenge: Existing approaches to extract attribute values from product descriptions are incomplete and noisy due to the tedious nature of this task.
Approach: They propose a framework to extract attributes from product descriptions to acquire implicit attributes in addition to the explicit ones.
Outcome: The proposed framework outperforms existing methods on the extraction of implicit attribute values while achieving comparable performance for the explicit ones.

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