MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation
<|reference_start|>MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation: 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.<|reference_end|>
@article{yang2024maple:,
    title={MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in
  Explainable Recommendation},
    author={Ching-Wen Yang, Che Wei Chen, Kun-da Wu, Hao Xu, Jui-Feng Yao, Hung-Yu
  Kao},
    journal={arXiv preprint arXiv:2408.09865},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09865},
    primaryClass={cs.LG cs.CL cs.IR}
}