Challenge: Existing recommendation system invites experts to write marketing themes and select relevant commodities, which suffer from difficulty in mass production, poor timeliness and low online indicators.
Approach: They propose to use pretrained language model to generate marketing themes and commodity consistency module to select relevant commodities for the generative theme.
Outcome: The proposed system can generate popular marketing themes and select relevant commodities automatically and improve theme online effectiveness compared with state-of-the-art methods.

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Challenge: e-Commerce websites are automatically generating millions of browse pages . manual creation of titles is infeasible due to the huge number of browse page types .
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Challenge: Product attribute extraction in e-commerce is bottlenecked by ontologies that are inconsistent, incomplete, and costly to maintain.
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Challenge: Existing approaches to generate general and aspect-specific opinion summarization are limited due to their reliance on human-specified aspects and seed words.
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