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Review-guided Answer Helpfulness Prediction

RAHP

2 papers tagged archive 2025-07-28

Introduced by Wenxuan Zhang et al. in Review-guided Helpful Answer Identification in E-commerce

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Review-guided Answer Helpfulness Prediction (RAHP) is a textual inference model for identifying helpful answers in e-commerce. It not only considers the interactions between QA pairs, but also investigates the opinion coherence between the answer and crowds' opinions reflected in the reviews, which is another important factor to identify helpful answers.

PaperSource

Papers archive 2025-07-28

2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Answer Selection1
Community Question Answering1
Graph Generation1
Large Language Model1
Question Answering1
Relation1
Scene Graph Generation1
Triplet1

Usage over time archive 2025-07-28

Papers per year tagged with RAHP: 2020 to 2024, peak 1 1 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 0 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Textual Inference Models

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