Reinforced Product Metadata Selection for Helpfulness Assessment of Customer Reviews (D19-1)
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| Challenge: | a helpful review is largely concerned with the metadata of its target product . a selector learns from both the key-value product metadata and one of its reviews to take an action . |
| Approach: | They propose a framework that uses product metadata to assess helpfulness of free-text reviews . they use two real-world datasets from amazon.com and Yelp.com to test the framework . |
| Outcome: | The proposed framework can achieve state-of-the-art performance with substantial improvements . it uses two real-world datasets from Amazon.com and Yelp.com . |
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| Challenge: | review helpfulness modeling is a task that studies the mechanisms that affect review helpfuliness and attempts to accurately predict it. |
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| Challenge: | Existing methods for detecting helpful reviews focus on review text and ignore the two key factors of (1) who post the reviews and (2) when the reviews are posted. |
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| Challenge: | Existing review helpfulness prediction tasks rely on text and image modalities to analyze review helpfuliness. |
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Identifying Helpful Sentences in Product Reviews (2021.naacl-main)
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| Challenge: | a key advantage of online shopping is the ability to read what other customers are saying about products of interest. |
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| Challenge: | e-commerce has become a research hotspot for review helpfulness prediction . a new approach to help predict helpfulness of multimodal product reviews is proposed . |
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Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Prediction (2022.emnlp-main)
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| Challenge: | Modern review helpfulness prediction systems focus on polishing cross-modal representations and suffer from inferior optimization. |
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