MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation

• URL: http://arxiv.org/abs/2408.09865v1
• Date: Mon, 19 Aug 2024 10:12:52 GMT
• Title: MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation
• Authors: Ching-Wen Yang, Che Wei Chen, Kun-da Wu, Hao Xu, Jui-Feng Yao, Hung-Yu Kao,
• Abstract summary: We propose a personalized, aspect-controlled model called Multi-Aspect Prompt LEarner (MAPLE) Experiments on two real-world review datasets in restaurant domain show that MAPLE outperforms the baseline review-generation models in terms of text.
• Score: 12.874105550787514
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Explainable Recommendation task is designed to receive a pair of user and item and output explanations to justify why an item is recommended to a user. Many models treat review-generation as a proxy of explainable recommendation. Although they are able to generate fluent and grammatical sentences, they suffer from generality and hallucination issues. We propose a personalized, aspect-controlled model called Multi-Aspect Prompt LEarner (MAPLE), in which it integrates aspect category as another input dimension to facilitate the memorization of fine-grained aspect terms. Experiments on two real-world review datasets in restaurant domain show that MAPLE outperforms the baseline review-generation models in terms of text and feature diversity while maintaining excellent coherence and factual relevance. We further treat MAPLE as a retriever component in the retriever-reader framework and employ a Large-Language Model (LLM) as the reader, showing that MAPLE's explanation along with the LLM's comprehension ability leads to enriched and personalized explanation as a result. We will release the code and data in this http upon acceptance.

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