@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://aclanthology.org/2025.acl-long.1535/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation
%A Yang, Ching-Wen
%A Feng, Zhi-Quan
%A Lin, Ying-Jia
%A Chen, Che Wei
%A Wu, Kun-da
%A Xu, Hao
%A Jui-Feng, Yao
%A Kao, Hung-Yu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F yang-etal-2025-maple
%X 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.
%R 10.18653/v1/2025.acl-long.1535
%U https://aclanthology.org/2025.acl-long.1535/
%U https://doi.org/10.18653/v1/2025.acl-long.1535
%P 31803-31821
Markdown (Informal)
[MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation](https://aclanthology.org/2025.acl-long.1535/) (Yang et al., ACL 2025)
ACL
- Ching-Wen Yang, Zhi-Quan Feng, Ying-Jia Lin, Che Wei Chen, Kun-da Wu, Hao Xu, Yao Jui-Feng, and Hung-Yu Kao. 2025. MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 31803–31821, Vienna, Austria. Association for Computational Linguistics.