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

arXiv: https://arxiv.org/abs/2408.09865 · HTML: https://mlsanity.com/paper/2408.09865/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets](https://mlsanity.com/paper/2007.09654/) (2020)
- [Personalized Transformer for Explainable Recommendation](https://mlsanity.com/paper/2105.11601/) (2021)
- [Neural Rating Regression with Abstractive Tips Generation for Recommendation](https://mlsanity.com/paper/1708.00154/) (2017)
- [Explainable Recommendation with Personalized Review Retrieval and Aspect Learning](https://mlsanity.com/paper/2306.12657/) (2023)
- [Factual and Informative Review Generation for Explainable Recommendation](https://mlsanity.com/paper/2209.12613/) (2022)
- [RAGE Against the Machine: Retrieval-Augmented LLM Explanations](https://mlsanity.com/paper/2405.13000/) (2024)
- [UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation](https://mlsanity.com/paper/2209.13885/) (2022)

## Built on by (most cited first, 3 of 3)

- [How Reliable are LLMs for Reasoning on the Re-ranking task?](https://mlsanity.com/paper/2508.18444/) (2025)
- [SUMFORU: An LLM-Based Review Summarization Framework for Personalized Purchase Decision Support](https://mlsanity.com/paper/2512.11755/) (2025)
- [Sensory-Aware Sequential Recommendation via Review-Distilled Representations](https://mlsanity.com/paper/2603.02709/) (2026)

## Abstract (arXiv)

The 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 precision 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 here https://github.com/Nana2929/MAPLE.git.