A prompting pipeline and statement-level metrics show that six state-of-the-art text-based explainable recommendation models achieve high semantic similarity but very low factual consistency on Amazon review data.
arXiv preprint arXiv:2508.20312 (2025)
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Statement-level ranking with an LLM-extracted, paraphrase-clustered Amazon benchmark shows popularity baselines often beat SOTA models in item-level personalized explanation ranking.
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On the Factual Consistency of Text-based Explainable Recommendation Models
A prompting pipeline and statement-level metrics show that six state-of-the-art text-based explainable recommendation models achieve high semantic similarity but very low factual consistency on Amazon review data.
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Rank, Don't Generate: Statement-level Ranking for Explainable Recommendation
Statement-level ranking with an LLM-extracted, paraphrase-clustered Amazon benchmark shows popularity baselines often beat SOTA models in item-level personalized explanation ranking.