Challenge: Recent scene marketing has proved effective for offline shopping.
Approach: They propose a novel product form, scene-based topic channel, which consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words.
Outcome: The proposed system can be automated and tested on a real-world e-commerce recommendation platform.

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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.
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QueryNER: Segmentation of E-commerce Queries (2024.lrec-main)

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Challenge: Prior work on aspect-value extraction has focused on extracting portions of a product title or query for narrowly defined aspects.
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Challenge: Existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies.
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Topic Modeling by Clustering Language Model Embeddings: Human Validation on an Industry Dataset (2022.emnlp-industry)

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Challenge: Topic models are powerful tools to get overview of large collections of text data.
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Topic Modeling: Contextual Token Embeddings Are All You Need (2024.findings-emnlp)

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Challenge: Current neural approaches to topic modeling have not been able to solve all of the problems.
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eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables (2025.naacl-industry)

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Challenge: eC-Tab2Text dataset is designed to capture product attributes and user-specific queries.
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A Structured Clustering Approach for Inducing Media Narratives (2026.acl-long)

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Challenge: Existing approaches to modeling media narratives miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability.
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A Query-Driven Topic Model (2021.findings-acl)

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Challenge: Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus.
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FABRIC: Fully-Automated Broad Intent Categorization in E-commerce (2025.emnlp-industry)

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Challenge: Existing query classification models have excellent predictive performance on single-intent queries, but there is little research on predicting multiple-intentions for broad queries.
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