Papers with E-Commerce

2 papers
Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce (N19-2)

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Challenge: Existing methods for short product title generation only consider textual information from long titles . MM-GAN incorporates image information and attribute tags from product, as well as textual info from original long titles.
Approach: They propose a multi-modal generative adversarial network for short product title generation in E-commerce . they incorporate image information and attribute tags from product, as well as textual information from original long titles .
Outcome: The proposed model outperforms state-of-the-art methods on a large-scale E-commerce dataset.
A Usage-centric Take on Intent Understanding in E-Commerce (2024.emnlp-main)

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Challenge: Identifying and understanding user intents is a crucial task for E-Commerce.
Approach: They propose to use intent understanding as a natural language reasoning task independent of product ontologies to identify and understand user intents.
Outcome: The proposed framework can't be used to strongly align user intents with products with desirable properties and recommend useful products across diverse categories.

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