@inproceedings{yang-etal-2025-maple,
    title = "{MAPLE}: Enhancing Review Generation with Multi-Aspect Prompt {LE}arning in Explainable Recommendation",
    author = "Yang, Ching-Wen  and
      Feng, Zhi-Quan  and
      Lin, Ying-Jia  and
      Chen, Che Wei  and
      Wu, Kun-da  and
      Xu, Hao  and
      Jui-Feng, Yao  and
      Kao, Hung-Yu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1535/",
    pages = "31803--31821",
    ISBN = "979-8-89176-251-0",
    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 approach review generation as a proxy for explainable recommendations. While these models can produce fluent and grammatically correct sentences, they often lack preciseness and fail to provide personalized informative recommendations. To address this issue, we propose a personalized, aspect-controlled model called Multi-Aspect Prompt LEarner (MAPLE), which integrates aspect category as another input dimension to facilitate memorizing fine-grained aspect terms. Experiments conducted on two real-world review datasets in the restaurant domain demonstrate that MAPLE significantly outperforms baseline review-generation models. MAPLE excels in both text and feature diversity, ensuring that the generated content covers a wide range of aspects. Additionally, MAPLE delivers good generation quality while maintaining strong coherence and factual relevance. The code and dataset used in this paper can be found at https://github.com/Nana2929/MAPLE."
}